Artificial Humans

VHAAI means Very Highly Advanced Artificial Intelligence. ISVHAAI means International Society for VHAAI. This is ISVHAAI Artificial Intelligence Society Letter No. 11. In this Letter, a novel algorithm titled Student Mobile Phone Human Swarm Optimization (SMPHSO) has been designed.

International Society for Very Highly Advanced Artificial Intelligence is the expansion of ISVHAAI. ISVHAAI is an Artificial Intelligence Society which uses Very Highly Advanced Artificial Intelligence (VHAAI) field for dealing with problems. A new algorithm titled Best Middle Worst Human Swarm Optimization (BMWHSO) is designed in this Letter No. 10 of ISVHAAI Artificial Intelligence Society Letters.

VHAAI means Very Highly Advanced Artificial Intelligence. ISVHAAI means International Society for VHAAI. VHAAI field is used by ISVHAAI Society in an attempt to solve problems. A new algorithm titled Friendship Human Swarm Optimization (FdHSO) is designed in this Letter No. 8 of ISVHAAI AI Society Letters.

VHAAI and ISVHAAI stands for Very Highly Advanced Artificial Intelligence and International Society for Very Highly Advanced Artificial Intelligence respectively. This is the ISVHAAI Artificial Intelligence Society Letter No. 7 in which a novel algorithm titled Family Human Swarm Optimization (FHSO) is designed.

The relation between Very Highly Advanced Artificial Intelligence (VHAAI) and International Society for Very Highly Advanced Artificial Intelligence (ISVHAAI) is that ISVHAAI AI Society uses VHAAI field to address various problems. In this Letter No. 6 of ISVHAAI Letters, a new algorithm titled "Scientist Swarm Optimization (SSO)" is designed.

Particle Swarm Optimization (PSO) is a popular and widely used optimization algorithm for solving complex problems. It is known for its simplicity and ease of implementation. Artificial Birds move in search space to find optimal solution. Although many PSO algorithms were proposed in literature the concepts like happiness and health are not yet explored in PSO algorithms. This article is based on this research gap. Happiness and Health Particle Swarm Optimization (HaHePSO) algorithm is created by incorporating the Happiness and Health concepts into Particle Swarm Optimization algorithm. Each particle in HaHePSO algorithm is associated with happiness and health variables. The movement of Artificial Birds in PSO algorithm is based on fitness values. In HaHePSO algorithm the movement of Artifical Birds is dependent on happiness, health and fitness values. In PSO algorithm Artificial Birds move in the direction of local best and global best of fitness values. This idea is extended in HaHePSO algorithm where Artificial Birds move in the direction of local best and global best of happiness, health and fitness values. The HaHePSO algorithm proposed in this article takes more space and requires extra computation compared to PSO algorithm. This is due to the fact that each particle now has happiness and health variables associated with it and movement in search space is guided by the fitness, happiness and health values.

Several Human-Inspired Metaheuristic Optimization Algorithms were proposed in literature. But the concept of Devotees-Inspired Metaheuristic Optimization Algorithms is not yet explored. In this article, Lord Rama Devotees Algorithm (LRDA) is proposed which is a new Devotees-Inspired Metaheuristic Optimization Algorithm.

Several Human-Inspired Metaheuristic Optimization Algorithms were proposed in literature. But the concept of Devotees-Inspired Metaheuristic Optimization Algorithms is not yet explored. In this article, Lord Rama Devotees Algorithm (LRDA) is proposed which is a new Devotees-Inspired Metaheuristic Optimization Algorithm.

John McCarthy (September 4, 1927 – October 24,2011) was an American computer scientist and cognitive scientist. The term “Artificial Intelligence” was coined by him(Wikipedia, 2020). Satish Gajawada (March 12, 1988 – Present) is an Indian Independent Inventor and Scientist. He coined the term “Artificial Satisfaction” in this article (Gajawada, S., and Hassan Mustafa, 2019a). A new field titled “Artificial Satisfaction” is introduced in this article. “Artificial Satisfaction” will be referred to as “The Brother of Artificial Intelligence” after the publication of this article. A new algorithm titled “Artificial Satisfaction Algorithm (ASA)” is designed and implemented in this work. For the sake of simplicity, Particle Swarm Optimization (PSO) Algorithm is modified with Artificial Satisfaction Concepts to create the“Artificial Satisfaction Algorithm (ASA).” PSO and AS Aalgorithms are applied on five benchmark functions. A comparision is made between the results obtained. The focus of...

The main idea to author this article is to "Popularize Artificial Human Optimization Field like never before by showing an Overview of this new field". This idea can be divided into following sub-ideas: 1) To show the definition of "Artificial Human Optimization Field (AHO Field)" 2) To show difficulty level of creating new algorithms under AHO field 3) To show 30+ titles of papers published under AHO field 4) To show names of 65+ authors who worked under AHO Field 5) To show best negative reviews obtained for work under AHO Field 6) To show best positive reviews obtained for work under AHO Field 7) To show feedback given by an expert for work under AHO Field 8) To show "Hassan Satish Particle Swarm Optimization (HSPSO)". This is latest work under AHO Field 9) To show contribution of Satish Gajawada and co-authors to this new Field 10) To show surprising results obtained after implementing AHO algorithms 11) To show you "Future of Artificial Human Optimization Field" 1 Sub-idea 1: Definition Artificial Human Optimization (AHO) is a new field proposed in December 2016. This work was published in Transactions on Machine Learning and Artificial Intelligence. All optimization methods which were proposed based on Artificial Humans will come under the new field titled Artificial Human Optimization [1]. The first paper in AHO field was proposed in 2006 [2]. 2 Sub-idea 2: Difficulty Level The following is the review obtained from an expert in 2013 for a work in AHO Field: "The motivation of the paper is interesting. But the paper does not present any evaluation of the proposed algorithm. So we have an idea but we are not able to assess it on the basis of the paper. Next, there seems to be a difference between birds, fishes, ants, bacteria, bees etc. on one side, and human beings on the

Computational intelligence poses Several possibilities in Bioinformatics, particularly by generating low-cost, lowprecision, good solutions. Rough sets promise to open up an important dimension in this direction. The present article surveys the role of artificial neural networks, fuzzy sets and genetic algorithms, with particular emphasis on rough sets, in Bioinformatics. Since the work entails processing huge amounts of incomplete or ambiguous biological data, the knowledge reduction capability of rough sets, Learning ability of neural networks, uncertainty handling capacity of fuzzy sets and searching potential of genetic algorithms are synergistically utilized.

Preface: In 20th and 21st Centuries the global optimization algorithms were created by taking inspiration from birds (Particle Swarm Optimization), ants (Ant Colony Optimization), chromosomes (Genetic Algorithms) etc. In “Twenty Second Century Artificial Intelligence” book global optimization algorithms are created by taking inspiration from Humans, Souls, Gods, Satisfied Beings, Mothers, Children, Particular Human Beings and Stories.

In 20th and 21st Centuries research scientists focused mainly on Brain Inspired Computing. In “Twenty Second Century Artificial Intelligence” book a new path is shown where algorithms are created by taking inspiration from both heart and brain.

In 20th and 21st Centuries the path of “Artificial Intelligence” is the main focus of research. In “Twenty Second Century Artificial Intelligence” book we defined “Artificial Satisfaction”.

In 20th and 21st Centuries researchers created many algorithms by taking inspiration from Nature (Nature Inspired Computing). In “Twenty Second Century Artificial Intelligence” book we created “Nature Plus Plus Inspired Computing”.

Abstract: The book defines various new paths as nine different chapters. First, second and third chapters deal with “Artificial Human Optimization”, “Artificial Soul Optimization” and “Artificial God Optimization” respectively.

Three new branches titled “Artificial Satisfaction”, “Deep Loving” and “Nature Plus Plus Inspired Computing” are shown in fourth, fifth and sixth chapters respectively.

The seventh chapter describes “Artificial Heart Neural Networks” where algorithms are created by taking inspiration from both Heart and Brain.

Two new branches “Artificial Excellence” and “Stories Inspired Optimization Algorithms” are created in last two chapters of this book.

The term "Nature Plus Plus Inspired Computing" is coined by us in this article. The abbreviation for this new term is "N++IC." Just like the C++ programming language is a superset of C programming language, Nature Plus Plus Inspired Computing (N++IC) field is a superset of the Nature Inspired Computing (NIC) field. We defined and introduced "Nature Plus Plus Inspired Computing Field" in this work. Several interesting opportunities in N++IC Field are shown for Artificial Intelligence Field Scientists and Students. We show a literature review of the N++IC Field after showing the definition of Nature Inspired Computing (NIC) Field. The primary purpose of publishing this innovative article is to show a new path to NIC Field Scientists so that they can come up with various innovative algorithms from scratch. As the focus of this article is to introduce N++IC to researchers across the globe, we added N++IC Field concepts to the Particle Swarm Optimization algorithm and created the "Children Cycle Riding Algorithm (CCR Algorithm)." Finally, results obtained by CCR Algorithm are shown, followed by Conclusions.

Artificial Human Optimization is a new field that came into existence on December 2016. All the optimization algorithms that were created and are being created based on Artificial Humans will come under Artificial Human Optimization Field. Just like agents in Ant Colony Optimization are Artificial Ants, agents in Bee Colony Optimization are Artificial Bees, agents in Genetic Algorithms are Artificial chromosomes, agents in Particle Swarm Optimization are Artificial Birds or Artificial Fishes, similarly agents in Artificial Human Optimization Algorithms are Artificial Humans. “Multiple Strategy Human Optimization (MSHO)” is a new algorithm designed recently based on Artificial Humans. The key concept in MSHO is to use more than one strategy in the optimization process. Two strategies are used in MSHO. One strategy is to move towards the best individual in one generation. Another strategy is to move away from the worst individual in next generation. Differential Evolution is a popular algorithm for solving optimization problems in various domains. In this paper “Human Inspired Differential Evolution (HIDE)” is proposed. The idea of HIDE algorithm is to use the concept of Multiple Strategies of MSHO algorithm in Differential Evolution. The mutation operator of Differential Evolution algorithm is modified to incorporate the key concept of MSHO algorithm in Differential Evolution. The proposed HIDE algorithm is tested by applying it on a complex benchmark problem.

Collection of Abstracts is a new kind of research paper where the complete focus is to show abstracts published in a particular field. Collection of Abstracts is generally published in conferences as pre-conference proceedings book and given to the authors on the day of conference. This kind of research papers give valuable information to researchers like “what are the titles? What are the abstracts? Who are the authors working in the field?”. The corresponding author of this paper published a paper titled “Entrepreneur : Artificial Human Optimization” where he collected 13 abstracts and shown them as it is in an attempt to propose a new field. This work was published in Transactions on Machine Learning and artificial Intelligence. Now, “Collection of Abstracts in Artificial Human Optimization Field” is the second research paper in the entire research industry, which is completely based on Abstracts of papers. The first 2 sections of this paper shows titles and abstracts of papers in Artificial Human Optimization Field. Third section shows corrections to previous work in Artificial Human Optimization Field.

The author proposed a new field titled “Artificial Human Optimization” in December 2016 [1]. He authored the following five articles in Artificial Human Optimization field:

1) Entrepreneur: Artificial Human Optimization. Transactions on Machine Learning and Artificial Intelligence, Volume 4 No 6 December (2016); pp: 64-70 [1]. 2) “CEO: Different Reviews on PhD in Artificial Intelligence”, Global Journal of Advanced Research, vol. 1, no.2, pp. 155-158, 2014 [2]. 3) “POSTDOC : The Human Optimization”, Computer Science & Information Technology (CS & IT), CSCP, pp. 183-187, 2013 [3]. 4) “Artificial Human Optimization – An Introduction”, Transactions on Machine Learning and Artificial Intelligence, Volume 6, No 2, pp: 1-9, April 2018 [4]. 5) “An Ocean of Opportunities in Artificial Human Optimization Field”, Transactions on Machine Learning and Artificial Intelligence, Volume 6, No 3, June 2018 [5].

The complete reviews of all the above papers are shown in this paper for the first time in Research Industry. This paper is a new kind of research paper where the focus is completely on the reviews obtained for a particular new field. This work is the extension of work in [2]. Similar to this paper, the article [2] is completely focused on reviews obtained. The difference between article [2] and this paper lies in the fact that in this paper reviews are shown for all the papers of the author in Artificial Human Optimization field whereas article [2] shows reviews of single paper in Artificial Human optimization field.

Global Optimization Techniques like Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization and other optimization techniques were used in literature to solve complex optimization problems. Many optimization algorithms were proposed in literature by taking the behavior of Birds, Ants, Fishes, Chromosomes etc. as inspiration. Recently, a new trend has begun in Evolutionary Computing Domain where optimization algorithms have been created by taking Human Behavior as inspiration. The focus of this paper is on optimization algorithms that were and are being created based on the behavior of Artificial Humans. In December 2016, a new field titled " Artificial Human Optimization " was proposed in literature. This paper is strongly meant to popularize " Artificial Human Optimization " field like never before by showing an Ocean of Opportunities that exists in this new and interesting area of research. A new field titled " Artificial Economics Optimization " is proposed at the end of paper.

A new field titled 'Artificial Human Optimization' is introduced in this paper. All optimization methods which were proposed based on Artificial Humans will come under this new field. Less than 20 papers were published in this field so far. The goal of this paper is to introduce 'Artificial Human Optimization' and to show abstracts of papers published in this new field. The nick name given to this work is 'ENTREPRENEUR'.

This paper is dedicated to everyone who is interested in the Artificial Intelligence. John Henry Holland proposed Genetic Algorithm in the early 1970s. Ant Colony Optimization was proposed by Marco Dorigo in 1992. Particle Swarm Optimization was introduced by Kennedy and Eberhart in 1995. Storn and Price introduced Differential Evolution in 1996. K.M. Passino introduced Bacterial Foraging Optimization Algorithm in 2002. In 2003, X.L. Li proposed Artificial Fish Swarm Algorithm. Artificial Bee Colony algorithm was introduced by Karaboga in 2005. In the past, researchers have explored behavior of chromosomes, birds, fishes, ants, bacteria, bees and so on to create excellent optimization methods for solving complex optimization problems. In this paper, Satish Gajawada proposed The Human Optimization. Humans progressed like anything. They help each other. There are so many plus points in Humans. In fact all optimization algorithms based on other beings are created by Humans. There is so much to explore in behavior of Human for creating awesome optimization algorithms. Artificial Fishes, birds, ants, bees etc have solved optimization problems. Similarly, optimization method based on Humans is expected to solve complex problems. This paper sets the trend for all optimization algorithms that come in future based on Humans.