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The Artificial Bee Colony (ABC) is a well-known simple and efficient bee inspired metaheuristic that has been showed to achieve good performance on real valued optimization problems. Inspired by such, a Quick Artificial Bee Colony (QABC) was proposed by Karaboga to enhance the global search and bring better analogy to the dynamic of bees. To improve its local search capabilities, a modified version of it, called Evolutionary Quick Artificial Bee Colony (EQABC), is proposed. The novel algorithm employs the mutation operators found in Evolutionary Strategies (ES) that was applied in ABC from Evolutionary Particle Swarm Optimization (EPSO). In order to test the performance of the new algorithm, it was applied in four large-scale constrained optimization structural engineering problems. The results obtained by EQABC are compared to original ABC, QABC, and ABC + ES, one of the algorithms inspired for the development of EQABC.

The following paper introduces a hybrid algorithm that combines Artificial Bee Colony Algorithm (ABC) and a model of Evolution Strategies (ES) found in the Evolutionary Particle Swarm Optimization (EPSO), another hybrid metaheuristic. The goal of this approach is to incorporate the effectiveness and simplicity of the ABC with the thorough local search mechanism of the Evolution Strategies in order to devise an algorithm that is able to achieve better optimality in less time than the original ABC applied to function optimization problems. With the intention of assessing this novel algorithm performance and reliability, several unconstrained benchmark functions as well as four large-scale constrained optimization-engineering problems (WBD, DPV, SRD-11 and MWTCS) act as an evaluation environment. The results obtained by the ABC+ES are compared to original ABC and several other optimization techniques.

This paper has the purpose of presenting a new hybridization of the Artificial Bee Colony Algorithm (ABC) based on the evolutionary strategies (ES) found on the Evolutionary Particle Swarm Optimization (EPSO). The main motivation of this approach is to augment the original ABC in a way that combines the effectiveness and simplicity of the ABC with the robustness and increased exploitation of the Evolution Strategies. The algorithm is intended to be tested on two large-scale engineering design problem and its results compared to other optimization techniques.

This paper has the purpose to present a new hybrid nature inspired metaheuristic developed based on three fundamentals pillars extremely well known: Genetic Algorithms, Game Theory and Fuzzy Systems. This new approach tries to mimic a little bit more closer how a population of individuals evolves along time, like human social evolution emphasizing the social interaction between individuals and the non-binary behavior of human decision making against the classical cooperate-defect behavior present in the Prisoner's Dilemma (PD), for example. In this way it is presented the Social Interaction Genetic Algorithm (SIGA), to establish the necessary basis for the application of fuzzy concepts to get the F-SIGA Algorithm. Besides that, it is also presented the structure of an individual more complex with a genotype composed of two chromosomes, one for the solution of the problem and the other representing its behavior's strategy, which could be binary or fuzzy. At least the F-SIGA approach is presented in details, including all its steps. And finally some results are presented to an instance of the Traveling Salesman Problem.

This work has the purpose to present a new hybrid metaheuristic developed based on three fundamentals pillars extremely well known: Genetic Algorithms, Game Theory and Fuzzy Systems. This new approach tries to mimic a little bit more closer how a population of individuals evolves along time, like human social evolution emphasizing the social interaction between individuals and the non-binary behavior of human decision making against the classical cooperate-defect behavior present in the Prisoner's Dilemma. In this way it is also presented the SIGA Algorithm [9], the approach of an individual more complex with a genotype composed of two chromosomes, one for the solution of the problem and the other representing its strategy, a binary or fuzzy. Finally some results are presented to an instance of the Traveling Salesman Problem.

In this paper, it is presented a new evolutionary algorithm of advanced optimization based in the technique of Artificial Immune Systems and in the Principles of Game Theory, more specifically, in the inclusion of evolutionary characteristics and a phase called "Social Interaction" in the algorithm AISO. In this way some theoretical aspects are presented, the new algorithm proposal and finally some simulation and comparison to Classical Genetic Algorithm and Genetic Algorithm with Social Interaction are made to the Traveling Salesman Problem.

In this paper, we present a Fuzzy Influence Function for the CAEP model (Cultural Algorithms with Evolutionary Programming) proposed by Chung [1] and extended by Zhu [6], applied to real-valued function optimization. The proposal makes use of a Fuzzy Inference System 'FIS' to adjust an Influence Factor that represents the intensity of the influence of the Variation operator of the CAEP model. This paper also presents a comparative analysis of the proposed influence function using a set of 17 of the CEC '05 benchmarking functions.

In this paper, it is presented a new way to characterize the phenotype in the context of Genetic Algorithms through the use of Game Theory as a theoretical foundation to define a new phase in the algorithm, named Social Interaction. It is executed before the reproduction phase and allows individuals to fight for their own survival improving their fitness according to the rules of a game. Thereby, a new algorithm is presented and some good results were produced for Traveling Salesman Problem an improvement in Genetic Algorithm execution.

Genetic Algorithms have been successful in several practical appliances and obtained great prominence among the techniques of Evolutionary Computation. A large portion of methods and parameters adopted in its use are heuristic or random choices, which remain unchanged throughout the execution once they are set. This can lead to a certain lack of exploration of the search surface and also compromise the variability of the population. For that matter, this work introduces the implementation of an intelligent agent based on fuzzy logic, enabling dynamic monitoring and regulation of six GA parameters. The results obtained surpass the GA traditional implementation in many aspects, and open a wide new space for research and study.