Evolutionary algorithms

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.

In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of AI. In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a 'good AI society'. To do so, we examine how each report addresses the following three topics: (a) the development of a 'good AI society'; (b) the role and responsibility of the government, the private sector, and the research community (including academia) in pursuing such a development; and (c) where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a 'good AI society'. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach.

The interest in nonparametric statistical analysis has grown recently in the field of computational intelligence. In many experimental studies, the lack of the required properties for a proper application of parametric procedures -independence, normality, and homoscedasticity -yields to nonparametric ones the task of performing a rigorous comparison among algorithms.

The objective of this work is to automatically recognize faces from video sequences in a realistic, unconstrained setup in which illumination conditions are extreme and greatly changing, viewpoint and user motion pattern have a wide variability, and video input is of low quality. At the centre of focus are face appearance manifolds: this thesis presents a significant advance of their understanding and application in the sphere of face recognition. The two main contributions are the Generic Shape-Illumination Manifold recognition algorithm and the Anisotropic Manifold Space clustering.

The Generic Shape-Illumination Manifold algorithm shows how video sequences of unconstrained head motion can be reliably compared in the presence of greatly changing imaging conditions. Our approach consists of combining a priori domain-specific knowledge in the form of a photometric model of image formation, with a statistical model of generic face appearance variation. One of the key ideas is the reillumination algorithm which takes two sequences of faces and produces a third, synthetic one, that contains the same poses as the first in the illumination of the second.

The Anisotropic Manifold Space clustering algorithm is proposed to automatically determine the cast of a feature-length film, without any dataset-specific training information. The method is based on modelling coherence of dissimilarities between appearance manifolds: it is shown how inter- and intra-personal similarities can be exploited by mapping each manifold into a single point in the manifold space. This concept allows for a useful interpretation of classical clustering approaches, which highlights their limitations. A superior method is proposed that uses a mixture-based generative model to hierarchically grow class boundaries corresponding to different individuals.

The Generic Shape-Illumination Manifold is evaluated on a large data corpus acquired in real-world conditions and its performance is shown to greatly exceed that of state-of-the-art methods in the literature and the best performing commercial software. Empirical evaluation of the Anisotropic Manifold Space clustering on a popular situation comedy is also described with excellent preliminary results.

This paper presents an analysis of 56 variants of European and North American examples of the so-called Polyphemus tale (international tale type ATU 1137) using phylogenetic softwares according to 190 traits. Discussion addresses a number of points of comparative methodology while considering the historical implications of a relationship between different versions of this tale type recorded in diverse cultures.

Jonas Krause, Jelson Cordeiro, Rafael Stubs Parpinelli, Heitor Silvério Lopes, A Survey of Swarm Algorithms Applied to Discrete Optimization Problems, In: Xin-She Yang, Zhihua Cui, Renbin Xiao, Amir Hossein Gandomi and Mehmet Karamanoglu, Editor(s), Swarm Intelligence and Bio-inspired Computation, Elsevier, Oxford, Pages 169-191, 2013.

This work proposes an enhanced particle swarm optimization scheme that improves upon the performance of the standard particle swarm optimization algorithm. The proposed algorithm is based on chaos search to solve the problems of stagnation, which is the problem of being trapped in a local optimum and with the risk of premature convergence. Type 1" constriction is incorporated to help strengthen the stability and quality of convergence, and adaptive learning coefficients are utilized to intensify the exploitation and exploration search characteristics of the algorithm. Several well known benchmark functions are operated to verify the effectiveness of the proposed method. The test performance of the proposed method is compared with those of other popular population-based algorithms in the literature. Simulation results clearly demonstrate that the proposed method exhibits faster convergence, escapes local minima, and avoids premature convergence and stagnation in a high-dimensional problem space. The validity of the proposed PSO algorithm is demonstrated using a fuzzy logic-based maximum power point tracking control model for a standalone solar photovoltaic system.

Today, in most of metal machining process, Computer Numerical Control (CNC) machine tools have been very popular due to their efficiencies and repeatability to achieve high accuracy positioning. One of the factors that govern the productivity is the tool path travel during cutting a work piece. It has been proved that determination of optimal cutting parameters can enhance the machining results to reach high efficiency and minimum the machining cost. In various publication and articles, scientist and researchers adapted several Artificial Intelligence (AI) methods or hybrid method for tool path optimization such as Genetic Algorithms (GA), Artificial Neural Network (ANN), Artificial Immune Systems (AIS), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). This study presents a review of researches in tool path optimization with different types of AI methods that show the capability of using different types of optimization methods in CNC machining process.

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

In this paper with the aid of genetic algorithm and fuzzy theory, we present a hybrid job scheduling approach, which considers the load balancing of the system and reduces total execution time and execution cost. We try to modify the standard Genetic algorithm and to reduce the iteration of creating population with the aid of fuzzy theory. The main goal of this research is to assign the jobs to the resources with considering the VM MIPS and length of jobs. The new algorithm assigns the jobs to the resources with considering the job length and resources capacities. We evaluate the performance of our approach with some famous cloud scheduling models. The results of the experiments show the efficiency of the proposed approach in term of execution time, execution cost and average Degree of Imbalance (DI).

Penulis ini bertujuan untuk Penjadwalan ruangan untuk setiap mata kuliah yang berada di LAB untuk setiap sesi dalam satu hari yang telah ditentukan oleh pihak Fakultas. Dengan menghitung jumlah mata kuliah yang sama dalam satu hari dan sesi waktu yang sama. Kemudian setiap semester semakin terus bertambah mata kuliah yang diadakan di LAB ICT. Pada kasus ini, penulis menggunakan metode algoritma genetika dengan pengkodean integer, lalu panjang kromosom sebanyak 14. Pengujian dilakukan untuk memudahkan pekerjaan supervisor dan dapat berupa solusi saran penjadwalan terbaik. Dari penulis yang telah dilakukan terhadap hasil pengujian penjadwalan didapatkan dari setiap sesi hari presentase keberhasilan sebesar 90.15%.

The article "Le motif du dragon serait paléolithique, mythologie et archéologie", published in Préhistoire du Sud-Ouest 21(2), 2013, 195-215. The article "Mythologie et statistique: Reconstructions, évolution et origines paléolithiques du combat contre le dragon", published in Mythologie Française 256, 2014, pp. 17-23. The article "Une méthode simple pour reconstruire une mythologie préhistorique (à propos de serpents mythiques sahariens)", published in Les Cahiers de l 'AARS 17, 2014, pp. 95-104.

Summary
In its parallel pursuit of an increased competitivity for design offices and more pleasurable and easier workflows for designers, artificial design intelligence is a technical, intellectual, and political challenge. While human-machine cooperation has become commonplace through Computer Aided Design (CAD) tools, a more improved collaboration and better support appear possible only through an endeavor into a kind of artificial design intelligence, which is more sensitive to the human perception of affairs.

Considered as part of the broader Computational Design studies, the research program of this quest can be called Artificial / Autonomous / Automated Design (AD). The current available level of Artificial Intelligence (AI) for design is limited and a viable aim for current AD would be to develop design assistants that are capable of producing drafts for various design tasks. Thus, the overall aim of this thesis is the development of approaches, techniques, and tools towards artificial design assistants that offer a capability for generating drafts for sub-tasks within design processes. The main technology explored for this aim is Evolutionary Computation (EC), and the target design domain is architecture. The two connected research questions of the study concern, first, the investigation of the ways to develop an architectural design assistant, and secondly, the utilization of EC for the development of such assistants.

While developing approaches, techniques, and computational tools for such an assistant, the study also carries out a broad theoretical investigation into the main problems, challenges, and requirements towards such assistants on a rather overall level. Therefore, the research is shaped as a parallel investigation of three main threads interwoven along several levels, moving from a more general level to specific applications. The three research threads comprise, first, theoretical discussions and speculations with regard to both existing literature and the proposals and applications of the thesis; secondly, proposals for descriptive and prescriptive models, mappings, summary illustrations, task structures, decomposition schemes, and integratory frameworks; and finally, experimental applications of these proposals. This tripartite progression allows an evaluation of each proposal both conceptually and practically; thereby, enabling a progressive improvement of the understanding regarding the research question, while producing concrete outputs on the way. Besides theoretical and interpretative examinations, the thesis investigates its subject through a set of practical and speculative proposals, which function as both research instruments and the outputs of the study. The first main output of the study is the “design_proxy” approach (d_p), which is an integrated approach for draft making design assistants. It is an outcome of both theoretical examinations and experimental applications, and proposes an integration of, (1) flexible and relaxed task definitions and representations (instead of strict formalisms), (2) intuitive interfaces that make use of usual design media, (3) evaluation of solution proposals through their similarity to given examples, and (4) a dynamic evolutionary approach for solution generation. The design_proxy approach may be useful for AD researchers that aim at developing practical design assistants, as has been examined and demonstrated with the two applications, i.e., design_proxy.graphics and design_proxy.layout.

The second main output, the “Interleaved Evolutionary Algorithm” (IEA, or Interleaved EA) is a novel evolutionary algorithm proposed and used as the underlying generative mechanism of design_proxy-based design assistants. The Interleaved EA is a dynamic, adaptive, and multi-objective EA, in which one of the objectives leads the evolution until its fitness progression stagnates; in the sense that the settings and fitness values of this objective is used for most evolutionary decisions. In this way, the Interleaved EA enables the use of different settings and operators for each of the objectives within an overall task, which would be the same for all objectives in a regular multi-objective EA. This property gives the algorithm a modular structure, which offers an improvable method for the utilization of domain-specific knowledge for each sub-task, i.e., objective. The Interleaved EA can be used by Evolutionary Computation (EC) researchers and by practitioners who employ EC for their tasks. As a third main output, the “Architectural Stem Cells Framework” is a conceptual framework for architectural design assistants. It proposes a dynamic and multi-layered method for combining a set of design assistants for larger tasks in architectural design. The first component of the framework is a layer-based, parallel task decomposition approach, which aims at obtaining a dynamic parallelization of sub-tasks within a more complicated problem. The second component of the framework is a conception for the development mechanisms for building drafts, i.e., Architectural Stem Cells (ASC). An ASC can be conceived as a semantically marked geometric structure, which contains the information that specifies the possibilities and constraints for how an abstract building may develop from an undetailed stage to a fully developed building draft. ASCs are required for re-integrating the separated task layers of an architectural problem through solution-based development. The ASC Framework brings together many of the ideas of this thesis for a practical research agenda and it is presented to the AD researchers in architecture.

Finally, the “design_proxy.layout” (d_p.layout) is an architectural layout design assistant based on the design_proxy approach and the IEA. The system uses a relaxed problem definition (producing draft layouts) and a flexible layout representation that permits the overlapping of design units and boundaries. User interaction with the system is carried out through intuitive 2D graphics and the functional evaluations are performed by measuring the similarity of a proposal to existing layouts. Functioning in an integrated manner, these properties make the system a practicable and enjoying design assistant, which was demonstrated through two workshop cases. The d_p.layout is a versatile and robust layout design assistant that can be used by architects in their design processes.

The use of metaheuristic search techniques for the automatic generation of test data has been a burgeoning interest for many researchers in recent years. Previous attempts to automate the test generation process have been limited, having been constrained by the size and complexity of software, and the basic fact that in general, test data generation is an undecidable problem. Metaheuristic search techniques offer much promise in regard to these problems. Metaheuristic search techniques are highlevel frameworks, which utilise heuristics to seek solutions for combinatorial problems at a reasonable computational cost. To date, metaheuristic search techniques have been applied to automate test data generation for structural and functional testing; the testing of grey-box properties, for example safety constraints; and also non-functional properties, such as worst case execution time. This paper surveys some of the work undertaken in this field, discussing possible new future directions of research for each of its different individual areas.

Real life optimization problems require techniques that properly explore the search spaces to obtain the best solutions. In this sense, it is common that traditional optimization algorithms fail in local optimal values. The Sine Cosine Algorithms (SCA) has been recently proposed; it is a global optimization approach based on two trigonometric functions. SCA uses the sine and cosine functions to modify a set of candidate solutions; such operators create a balance between exploration and exploitation of the search space. However, like other similar approaches, SCA tends to be stuck into sub-optimal regions that it is reflected in the computational effort required to find the best values. This situation occurs due that the operators used for exploration do not work well to analyze the search space. This paper presents an improved version of SCA that considers the opposition based learning (OBL) as a mechanism for a better exploration of the search space generating more accurate solutions. OBL is a machine learning strategy commonly used to increase the performance of metaheuristic algorithms. OBL considers the opposite position of a solution in the search space. Based on the objective function value, the OBL selects the best element between the original solution and its opposite position; this task increases the accuracy of the optimization process. The hybridization of concepts from different fields is crucial in intelligent and expert systems; it helps to combine the advantages of algorithms to generate more efficient approaches. The proposed method is an example of this combination; it has been tested over several benchmark functions and engineering problems. Such results support the efficacy of the proposed approach to find the optimal solutions in complex search spaces.

In this paper, a new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The main idea is to integrate the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both algorithms’ strength. Some benchmark test functions are used to compare the hybrid algorithm with both the standard PSO and GSA algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard PSO and GSA.

A B S T R A C T Scientists and decision-makers need tools that can assess which specific pressures lead to ecosystem deterioration , and which measures could reduce these pressures and/or limit their effects. In this context, species distribution models are tools that can be used to help asses these pressures. Evolutionary algorithms represent a collection of promising techniques, inspired by concepts observed in natural evolution, to support the development of species distribution models. They are suited to solve non-trivial tasks, such as the calibration of parameter-rich models, the reduction of model complexity by feature selection and/or the optimization of hy-perparameters of other machine learning algorithms. Although widely used in other scientific domains, the full potential of evolutionary algorithms has yet to be explored for applied ecological research. In this synthesis, we study the role of evolutionary algorithms as a machine learning technique to develop the next generation of species distribution models. To do so, we review available methods for species distribution modelling and synthesize literature using evolutionary algorithms. In addition, we discuss specific advantages and weaknesses of evolutionary algorithms and present a guideline for their application. We find that evolutionary algorithms are increasingly used to solve specific and challenging problems. Their flexibility, adaptability and transfer-ability in addition to their capacity to find adequate solutions to complex, non-linear problems are considered as main strengths, especially for species distribution models with a large degree of complexity. The need for programming and modelling skills can be considered as a drawback for novice modellers. In addition, setting values for hyperparameters is a challenge. Future ecological research should focus on exploring the potential of evolutionary algorithms that combine multiple tasks in one learning cycle. In addition, studies should focus on the use of novel machine learning schemes (e.g. automated hyperparameter optimization) to apply evolutionary algorithms, preferably in the context of open science. This way, ecologists and model developers can achieve an adaptable and flexible framework for developing tools useful for decision management.

The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as multi-objective optimization problems (MOPs)) has attracted much attention. Being population based approaches, EAs offer a means to find a group of Pareto-optimal solutions in a single run. Differential evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. The objective of

An algorithm is any well-defined procedure or set of instructions, that takes some input in the form of some values, processes them and gives some values as output. Sorting involves rearranging information into either ascending or descending order. Sorting is considered as a fundamental operation in computer science as it is used as an intermediate step in many operations. A new sorting algorithm namely 'An End-to-End Bi-directional Sorting (EEBS) Algorithm' is proposed to address the shortcomings of the current popular sorting algorithms. The goal of this research is to perform an extensive empirical analysis of the newly developed algorithm and present its functionality. The results of the analysis proved that EEBS is much more efficient than the other algorithms having O(n 2) complexity, like bubble, selection and insertion sort.

We introduce a new nature-inspired optimization algorithm namely Mushroom Reproduction Optimization (MRO) inspired and motivated by the reproduction and growth mechanisms of mushrooms in nature. MRO follows the process of discovering rich areas (containing good living conditions) by spores to grow and develop their own colonies. We thoroughly assess MRO performance based on numerous unimodal and multimodal benchmark functions as well as engineering problem instances. Moreover, to further investigate on the performance of the proposed MRO algorithm, we conduct a useful statistical evaluation and comparison with well known meta-heuristic algorithms. The experimental results confirm the high performance of MRO in dealing with complex optimization problems by discovering solutions with better quality.

Evolutionary algorithm (EA) is a generic population-based metaheuristic optimization algorithm. An EA uses some mechanisms inspired by biological evolution: reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the tness function determines the environment within which the solutions "live" . Evolution of the population then takes place after the repeated application of the above operators.
This description is deliberately based on a unifying view resenting a general scheme that forms the common basis of all volutionary Algorithm variants. The main components of EAs are discussed, explaining their role and related issues of terminology. Further on we discuss general issues for EAs concerning their working. Finally, we put EAs into a broader context and explain their relation with other global optimisation techniques.

Test functions are important to validate and compare the performance of optimization algorithms. There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions. Ideally, test functions should have diverse properties so that can be truly useful to test new algorithms in an unbiased way. For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimization problems with diverse properties in terms of modality, separability, and valley landscape. This is by far the most complete set of functions so far in the literature, and tt can be expected this complete set of functions can be used for validation of new optimization in the future.

Computer numerical control (CNC) machines have been widely used in automotive manufacturing industries especially of machining operation in automotive part such as engine body
and cylinder. One of the key features that improve efficiency of CNC machining is through the
optimization of tool path. Previous researcher to optimize tool path has premeditated several approaches. This paper aims to provide a critical review of those approaches that have been
developed in tool path. The developed tool path approaches covered different types of machining
process under various constraints condition. This paper focuses on tool path generation in CNC machining such as milling and cutting process. Based on our finding, this review paper collects information on tool path optimization and recommends future research direction.

Network design problem has been, and is, an important  problem in transportation. Following an earlier effort in designing a meta-heuristic search technique by an ant system, this paper attempts to hybridize this concept with other meta-heuristic concepts such as genetic algorithm, simulated annealing, and tabu search. Seven hybrids have been devised and tested on the network of Sioux Falls. It has been observed that the hybrids are more effective to solve the network design problem than the base ant system. Application of the hybrid containing all four concepts on a real network of a city with over 2 million population has also proved to be more effective than the base network, in the sense of finding better solutions sooner.

We developed a prediction model based on the evolutionary causal matrices (ECM) and the Markov Chain to predict long-term influences of educational interventions on adolescents’ development. Particularly, we created a computational model predicting longitudinal influences of different types of stories of moral exemplars on adolescents’ voluntary service participation. We tested whether the developed prediction model can properly predict a long-term longitudinal trend of change in voluntary service participation rate by comparing prediction results and surveyed data. Furthermore, we examined which type of intervention would most effectively promote service engagement and what is the minimum required frequency of intervention to produce a large effect. We discussed the implications of the developed prediction model in educational interventions based on educational neuroscience.

Nature has provided rich models for computational problem solving, including optimizations based on the swarm intelligence exhibited by fireflies, bats, and ants. These models can stimulate computer scientists to think nontraditionally in creating tools to address application design challenges. Most if not all engineering tasks involve decisions about the product, service, and system design, which are related in some way to optimizing time and resources as well achieving balance between maximizing performance, profit, sustainability, quality, safety, and efficiency and minimizing cost, energy consumption, defects, and environmental impact. As the sidebar " Elements of an Optimization Problem " describes, many of these design problems have multiple objectives bound by highly complex constraints. The traditional approach of specializing design method to problem type does not fit well with complex, nonlinear problems, such as multicriteria engineering designs and multiple complex feature extraction in big data. This lack of suitability has motivated interest in more novel optimization approaches. One emerging trend is to combine heuristic search with multiagent systems to solve real-world business and engineering problems. 1 Such a combination has accuracy, efficiency, and performance advantages over specialized methods. For example, it can be used to more efficiently and accurately optimize or tune parameters in artificial neural networks, which are essential to many AI tasks. One class of novel optimization algorithms is based on swarm intelligence (SI). SI captures the idea that decision making among organisms in a community, such as ants and bees, uses local information and interactions with other agents and with their own environment, which in turn could be responsible for the rise of collective or social intelligence. One hypothesis is that complex interactions directly or indirectly contribute to the emergence of intelligence in highly developed biological species. The reasoning is that biological change results from the organism responding and adapting to alterations in its community and environment. Groups of

( Alternatif Link download Buku Pemrograman GPU: http://bit.ly/2R4ktSO ) Buku ini memberikan penjabaran dari konsep dasar sampai tingkat lanjut, mulai dari bagaimana menggunakan teknik parallel berbasis GPU, lalu mengimplementasikan terkait beberapa kebutuhan teknologi terkini, yang meliputi deep learning, self-driving cars, virtual reality, game development, accelerated computing, design & visualization, automous machines dan lainnya, dengan melakukan komputasi secara cepat dan handal, serta membuat pengembang mampu mengintegrasikan teknik parallel dengan teknologi saat ini maupun untuk masa depan dengan mengambil potensi cara kerja proses paralel GPU yang sangat cepat dalam pengolahan data untuk mem-bantu meningkatkan hasil yang lebih akurat.

An approach using a differential evolution (DE) optimization algorithm is proposed to optimize design parameters for improving the optical absorption efficiency of plasmonic solar cells (PSC). This approach is based on formulating the parameters extraction as a search and optimization process in order to maximize the optical absorption in the PSC. Determining the physical parameters of three-dimensional (3-D) PSC is critical for designing and estimating their performance, however, due to the complex design of the PSC, parameters extraction is time and calculation intensive. In this paper, this technique is demonstrated for the case of commercial thin-film hydrogenated amorphous silicon (a-Si:H) solar photovoltaic cells enhanced through patterned silver nano-disk plasmonic structures. The DE optimization of PSC structures was performed to execute a real-time parameter search and optimization. The predicted optical enhancement (OE) in optical absorption in the active layer of the PSC for AM-1.5 solar spectrum was found to be over 19.45% higher compared to the reference cells. The proposed technique offers higher accuracy and automates the tuning of control parameters of PSC in a time-efficient manner.

Recent discussions of the relationship between humankind and technology have been shaped by ideas about embodiment and corporeality. Social and technological advances such as the availability of new media devices, urbanization, and globalization have led to the constant reassessment of social values, including humankind’s place within nature, the relationship between the organic and the artificial and the impact of technology upon national identity and cultural production. As technology continues to gain momentum in pertinence and availability, contemporary artists have used their art to express sophisticated ideas about the possibilities of a machine-dominated society.

This thesis discusses contemporary New Media artworks that express, challenge, or celebrate the role of technology in society. Chapters focus on: a) conceptions of embodiment in a self-consciously media-driven ‘information society’, b) the use of new media in art for the purpose of challenging stereotypes relating to gender identity, and c) artworks that provide a fresh perspective on the connection between embodiment and
corporeality in the relationship between humans and non-humans (including the ecological consequences of redefining humankind’s relationship to the natural world). The thesis focuses solely on artworks by Japanese artists. It is argued that Japan’s economic history combined with its high levels of urbanization and media-driven culture offer a unique perspective on notions of embodiment and corporeality in the 21st century.
Furthermore, the thesis shows that the background influence of Shinto beliefs regarding human-technology cohabitation has helped to shape a distinctive approach to the creative possibilities of contemporary new media art.

With Isabelle Dupanloup.

For the first time we are proposing an attempt to quantify the contribution of African and European folklores to the elaboration of an African American narrative : The Kind and the Unkind Girls. We have based our findings on the work of Dupanloup and Bertorelle (2001) for estimating the relative contribution of many parental populations to a hybrid group. The results highlighted the existence of an essentially vertical transmission in certain oral traditions that could be correlated with the genetic transmission within populations that know this story, and the capacity of oral traditions to resist external cultural pressures.

Nous proposons ici, pour la première fois, de quantifier la part prise par le folklore africain et le folklore européen dans l'élaboration d'un récit afroaméricain. Nous nous sommes pour cela appuyé sur la méthode développée par Dupanloup et Bertorelle (2001) qui permet d’estimer les contributions de populations parentales à une population métissée. Les résultats montrent l'existence d'une transmission essentiellement verticale dans certaines traditions orales, possiblement corrélées à la transmission génétique, ainsi que la capacité, pour une tradition, de survivre à de fortes pressions extérieures.

Combinatorial optimization problems are typically NP-hard, and thus very challenging to solve. In this paper, we present the random key cuckoo search (RKCS) algorithm for solving the famous Travelling Salesman Problem (TSP). We used a simplified random-key encoding scheme to pass from a continuous space (real numbers) to a combinatorial space. We also consider the displacement of a solution in both spaces using Lévy flights. The performance of the proposed RKCS is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that RKCS is superior to some other meta-heuristic algorithms.

Flexible job shop scheduling problems (FJSP) have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence (SI) and evolutionary algorithms (EA) are developed, employed and improved for solving them. More than 60% of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population and local search operators for improving convergence performance are summarized. Next, one classical hybrid genetic algorithm (GA) and one newest imperialist competitive algorithm (ICA) with variables neighborhood search (VNS) for solving FJSP are presented. Finally, we summarize, discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

"Abstract: Mythology, like genetics and language, provides essentials elements in the understanding of human history; phylogenetic trees based on mythological versions can allow to reconstruct the history and prehistory of human cultures right back the Palaeolithic period. Indeed, mythological versions are highly conservative. We also can reconstruct the primitive version of a mythological family using phylogenetic reconstructions of ancestral state.

Résume: La mythologie, comme la génétique et les langues, fournit des éléments essentiels à la compréhension de l'histoire humaine. Des arbres phylogénétiques basés sur des versions mythologiques peuvent permettre de reconstituer l'histoire et la préhistoire de cultures humaines en remontant jusqu'à la période paléolithique. En effet, les versions mythologiques sont très conservatrices. Nous pouvons également reconstruire la version primitive d'une famille mythologique à l'aide de reconstructions phylogénétiques d'un état ​​ancestral.

"

Network design problem has been, and is, an important problem in transportation. Following an earlier effort in designing a meta-heuristic search technique by an ant system, this paper attempts to hybridize this concept with other meta-heuristic concepts such as genetic algorithm, simulated annealing, and tabu search. Seven hybrids have been devised and tested on the network of Sioux Falls. It has been observed that the hybrids are more effective to solve the network design problem than the base ant system. Application of the hybrid containing all four concepts on a real network of a city with over 2 million population has also proved to be more effective than the base network, in the sense of finding better solutions sooner.

The purpose of this article is to present a novel genetic programming trading technique in the task of forecasting the next day returns when trading the EUR/USD exchange rate based on the exchange rates of historical data. Aiming at testing its effectiveness, we benchmark the forecasting performance of our genetic programming implementation with three traditional strategies (naive strategy, MACD, and a buy & hold strategy) plus a hybrid evolutionary artificial neural network approach. The proposed genetic programming technique was found to demonstrate the highest trading performance in terms of annualized return and information ratio when compared to all other strategies which have been used. When more elaborate trading techniques, such as leverage, were combined with the examined models, the genetic programming approach still presented the highest trading performance. To the best

One of the most important digital image processing steps in the remote sensing is
undoubtedly classification. As a branch of classification, unsupervised classification is a general
concept which defines natural structures and groups in data without training. The K-means (KM)
and Fuzzy C-means (FCM) are the most common unsupervised classification methods. For over
the last two decades, Artificial Intelligence (AI) optimization algorithms (heuristic algorithms)
such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE)
have been using for different nonlinear problems in many disciplines successfully. As an AI
optimization algorithm, Artificial Bee Colony (ABC) was recently proposed. PSO is inspired from
the social behaviors of bird and fish shoals, GA is similar to the evolutionary process in the nature,
DE is a GA-based intuitional algorithm and ABC which is inspired from honey bees. These AI
optimization algorithms are especially preferred when the classical deterministic methods are
inadequate because of too many parameters and data sets are not homogenous. These AI tools are
effectively used in remote sensing, as well. The main objective of this study is to examine the
effectiveness of AI optimization algorithms for unsupervised classification problem. Especially the
potential of the ABC among others is the most overrating point of this study. The basic approach
for unsupervised classification is to determine the centers of clusters of user specified number by
using pattern similarities based on the Euclid norm. As dataset, beside to Landsat TM imagery,
some benchmark data named Wine, Breast Tissue and Image Segmentation obtained from UCI database are used. The performances of AI tools are compared with conventional KM and FCM

Artificial bee colony algorithm is one of the most recently proposed swarm intelligence based optimization algorithm. A memetic algorithm which combines Hooke–Jeeves pattern search with artificial bee colony algorithm is proposed for numerical global optimization. There are two alternative phases of the proposed algorithm: the exploration phase realized by artificial bee colony algorithm and the exploitation phase completed by pattern search. The proposed algorithm was tested on a comprehensive set of benchmark functions, encompassing a wide range of dimensionality. Results show that the new algorithm is promising in terms of convergence speed, solution accuracy and success rate. The performance of artificial bee colony algorithm is much improved by introducing a pattern search method, especially in handling functions having narrow curving valley, functions with high eccentric ellipse and some complex multimodal functions.► We propose a memetic artificial bee colony algorithm for numerical optimization. ► Pattern search is introduced into the basic ABC to improve its performance. ► There are two alternative phases of the proposed algorithm. ► The proposed algorithm was tested on a comprehensive set of benchmark functions. ► The performance of the new algorithm is compared with several other algorithms.

Despite the growing popularity of swarm-based applications of drones, there is still a lack of approaches to maximize the safety of swarms of drones by minimizing the risks of drone collisions. In this paper, we present an approach that uses optimization, learning, and automatic immediate responses (reflexes) of drones to ensure safe operations of swarms of drones. The proposed approach integrates a high-performance dynamic evolutionary algorithm and a reinforcement learning algorithm to generate safe and efficient drone routes and then augments the generated routes with dynamically computed drone reflexes to prevent collisions with unforeseen obstacles in the flying zone. We also present a parallel implementation of the proposed approach and evaluate it against two benchmarks. The results show that the proposed approach maximizes safety and generates highly efficient drone routes.

In this paper, we analyze four dominance criteria in terms of their ability to adequately order sets of points in multiand many-objective optimization problems. The use of relaxed and alternative dominance relationships has been an important tool for improving the performance of multiobjective evolutionary optimization algorithms, and their ordering ability is among the most important characteristics responsible for such improvement. Three relaxed formulations of dominance are investigated, along with the traditional Pareto ordering, in order to provide a comparison baseline. The results obtained show that all three relaxed dominance approaches presented greater robustness to the increase in the number of objectives, and are therefore more appropriate for use in many-objective optimization algorithms.

In our information societies, we increasingly delegate tasks and decisions to automated systems, devices and agents that mediate human relationships, by taking decisions and acting on the basis of algorithms. Their increased intelligence, autonomous behavior and connectivity are changing crucially the life conditions of human beings as well as altering traditional concepts and ways of understanding reality. Algorithms are directed to solve problems that are not always detectable in their own relevance and timeliness. They are also meant to solve those problems through procedures that are not always visible and assessable in their own. In addition, technologies based on algorithmic procedures more and more infer personal information from aggregated data, thus profiling human beings and anticipating their expectations, views and behaviors. This may have normative, if not discriminatory, consequences. While algorithmic procedures and applications are meant to serve human needs, they risk to create an environment in which human beings tend to develop adaptive strategies by conforming their behaviour to the expected output of the procedures, with serious distortive effects. Against this backdrop, little room is often left for a process of rational argumentation able to challenge the results of algorithmic procedures by putting into question some of their hidden assumptions or by taking into account some neglected aspects of the problems under consideration. At the same time, it is widely recognized that scientific and social advances crucially depend on such an open and free critical discussion.