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The functionality of Evolutionary Algorithms should be better by way of integrating the idea that of brokers. brokers and Multi-agents can convey many fascinating beneficial properties that are past the scope of conventional evolutionary technique and learning.

This publication provides the state-of-the artwork within the idea and perform of Agent dependent Evolutionary seek and goals to extend the notice in this powerful expertise. This comprises novel frameworks, a convergence and complexity research, in addition to real-world purposes of Agent established Evolutionary seek, a layout of multi-agent architectures and a layout of agent verbal exchange and studying method.

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Additional info for Agent-Based Evolutionary Search (Adaptation, Learning, and Optimization, Volume 5)

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For f1, f5 and f6, the solutions of MAGA are better than those of OGA/Q, and for the other functions, the solutions of MAGA are as good as those of OGA/Q. Moreover, the mean number of function evaluations of MAGA is about 10,000 for all functions, while that of OAGA/Q ranges from 100,000 to 300,000. Therefore, the computational cost of MAGA is much lower than those of OGA/Q. To summarize, the results show that MAGA outperforms FEP and OGA/Q, and is competent for the numerical optimization problems.

To represent practical problems appropriately, many mathematical optimization models require equality constraints in addition to inequality constraints. The existence of equality constraints reduces the size of the feasible space, which makes it difficult to locate feasible and optimal solutions. This paper shows the enhanced performance of an agent-based evolutionary algorithm in solving Constrained Optimization Problems (COPs) with equality constraints. In the early generations of the evolutionary process, the agents use a new learning process that is specifically designed for handling equality constraints.

MAs are inspired by Dawkins’ notion of a meme [22], defined as a unit of information that reproduces itself as people exchange ideas. In this paper, the concept of MAs follows the model of adaptation in natural systems, where an individual of a population may be improved thorough self learning along with the evolutionary adaptation of the population [23, 24]. Some theoretical and empirical investigations on MAs can be found in [21, 23, 25-30]. One of the critical issues regarding the performance of MAs is the selection of appropriate local search methods while hybridizing local search with GAs.

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