Developing Redundant Binary Representations for Genetic Search

HAL (Le Centre pour la Communication Scientifique Directe), 2006

First, we explain why Genetic Algorithms (GAs), inspired by the Modern Synthesis, do not accurately model biological evolution, being rather an artificial version of artificial, rather than natural selection. Being focused on optimisation, we propose two improvements of GAs, with the aim to successfully generate adapted, desired behaviour. The first one concerns phylogenetic grounding of meaning, a way to avoid the Symbol Grounding Problem. We give a definition of Phylogenetically Acquired Representations, based on a parallel between the notions of representation and of adaptation. In the second part of the paper, we propose a hybrid version of genetic algorithms, differently organizing the flow of genetic information by introducing inheritance of acquired traits and Horizontal Gene Transfer, a good tool for handle a cumulative directional process of artificial selection.

IEEE Transactions on Evolutionary Computation, 2000

One of the most influential factors in the quality of the solutions found by an evolutionary algorithm are a correct coding of the search space and an appropriate evaluation function of the potential solutions. Evolutionary algorithms are often used to learn decision rules from datasets, which are encoded as individuals in the genetic population. In this paper, the coding of the search space for the obtaining of those decision rules is approached, i.e., the representation of the individuals of the genetic population and also the design of specific genetic operators. Our approach, called "natural coding", uses one gene per feature in the dataset (continuous or discrete). The examples from the datasets are also encoded into the search space, where the genetic population evolves, and therefore the evaluation process is improved substantially. Genetic operators for the natural coding are formally defined as algebraic expressions.

Evolvable Hardware, 2002

The use of adjacency matrices to the chromosome coding in evolutionary circuits techniques is proposed. The approach is shown to overcome some of the drawbacks associated with other coding schemes simplifying the implementation of the evolution process. It is particularly shown that the proposed coding scheme reduces considerably the generation of anomalous circuits increasing the efficiency of the overall process.

1996

In DNA-based computation, the problem in-stances are encoded in DNA oligonucleotides that must hybridize correctly to produce a solution. Depending on reaction conditions, oligonucleotides can bind with imperfect match-ing of complementary base pairs. These mis-matched hybridizations are a potential source of errors. For reliable DNA-based computation, the encodings should be a minimum distance apart. This distance could be estimated from

Lecture Notes in Computer Science, 2003

To select an adequate coding is one of the main problems in applications based on Evolutionary Algorithms. Many codings have been proposed to represent the search space for obtaining decision rules. A suitable representation of the individuals of the genetic population can reduce the search space, so that the learning process is accelerated by decreasing the number of necessary generations to complete the task. In this sense, natural coding achieves such reduction and improves the results obtained by other codings. This paper justifies the use of natural coding by comparing it with hybrid coding that joins well-known binary and real representations. We have tested both codings on a heterogeneous subset of databases from the UCI Machine Learning Repository. The experiments' results show that natural coding improves the quality of the obtained knowledge-model using only one third of the generations that hybrid coding needs as well as a smaller population.

Gecco, 1999

This paper demonstrates the ability of genetic programming to evolve analog circuits that perform digital functions and mixed analog-digital circuits. The evolved circuits include two purely digital circuits (a 100 nano-second NAND circuit and a two-instruction arithmetic logic unit circuit) and one mixed-signal circuit, namely a three-input digital-to-analog converter.

Proceedings of the 9th Joint Conference on Information Sciences (JCIS), 2006

In this paper, we propose a new genetic algorithm based on the two-dimensional encoding method. Appropriate two-dimensional crossover and mutation operations are designed based on the two-dimensional representation to generate the next generations. A twodimensional repairing mechanism is also proposed to adjust infeasible chromosomes into feasible ones. Experiments are finally made to show the effectiveness of the proposed genetic algorithm.

2000

The success of decoder-based evolutionary algorithms (EAs) strongly depends on the achieved locality of operators and decoders. Most approaches to investigate locality properties are static and consider only parts of the complex interactions within an EA, and sometimes, these techniques give misleading results. We suggest an explicit analysis of the dynamic behavior, emphasizing the effects of locality on evolutionary search. The impact of our methodology is twofold since it confirms previous statically obtained results and allows to gain reliable additional insight about the actual dynamics. The approach is successfully applied to four EAs for the multidimensional knapsack problem, and it can easily be adapted to other problems and EAs.

2007

In this paper we present a Common Genetic Encoding (CGE) for networks that can be applied to both direct and indirect encoding methods. As a direct encoding method, CGE allows the implicit evaluation of an encoded phenotype without the need to decode the phenotype from the genotype. On the other hand, one can easily decode the structure of a phenotype network, since its topology is implicitly encoded in the genotype's gene-order. Furthermore, we illustrate how CGE can be used for the indirect encoding of networks. CGE has useful properties that makes it suitable for evolving neural networks. A formal definition of the encoding is given, and some of the important properties of the encoding are proven such as its closure under mutation operators, its completeness in representing any phenotype network, and the existence of an algorithm that can evaluate any given phenotype without running into an infinite loop.

Origins of life and evolution of the biosphere : the journal of the International Society for the Study of the Origin of Life, 2015

The distribution of the triplet to amino acid correspondences in the genetic code matrix contains blocks of similarity. There are (a) groups of similar triplets coding for the same amino acid, which is called code degeneracy, and (b) clusters of similar amino acids corresponding to similar triplets. Processes that led to this regionalization have been investigated through a variety of perspectives but no consensus has been reached and no model has been convincing enough to drive experimental tests. Most traditional has been the hypothesis that the code was derived from the standard evolutionary processes of testing variations in the correspondences through the fitness measure of reaching distributions in the matrix space in an optimal manner so that the effects of mutations on protein phenotypes would be minimized, that is, with reduction of the intensity or of the deviant quality of the functional alterations associated with variations. In contrast, the self-referential model for t...