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Evolutionary computation
About: Evolutionary computation is a research topic. Over the lifetime, 16918 publications have been published within this topic receiving 639068 citations.
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TL;DR: The Bat Algorithm as mentioned in this paper is based on the echolocation behavior of bats and combines the advantages of existing algorithms into the new bat algorithm to solve many tough optimization problems.
Abstract: Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.
3,528 citations
TL;DR: This paper proposes a self- Adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions.
Abstract: Differential evolution (DE) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DE in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies and their associated control parameter values. Employing a trial-and-error scheme to search for the most suitable strategy and its associated parameter settings requires high computational costs. Moreover, at different stages of evolution, different strategies coupled with different parameter settings may be required in order to achieve the best performance. In this paper, we propose a self-adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings can be determined adaptively to match different phases of the search process/evolution. The performance of the SaDE algorithm is extensively evaluated (using codes available from P. N. Suganthan) on a suite of 26 bound-constrained numerical optimization problems and compares favorably with the conventional DE and several state-of-the-art parameter adaptive DE variants.
3,085 citations
01 Sep 1999
TL;DR: It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.
Abstract: Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.
2,877 citations
11 Jan 1996
TL;DR: Introduction PART I: A COMPARISON of EVOLUTIONARY ALGORITHMS 1. Organic Evolution and Problem Solving 2. Specific Evolutionary Algorithms 3. Artificial Landscapes 4. An Empirical Comparison 5. Selection 6. Mutation 7. An Experiment in Meta-Evolution
Abstract: Introduction PART I: A COMPARISON OF EVOLUTIONARY ALGORITHMS 1. Organic Evolution and Problem Solving 2. Specific Evolutionary Algorithms 3. Artificial Landscapes 4. An Empirical Comparison PART II: EXTENDING GENETIC ALGORITHMS 5. Selection 6. Mutation 7. An Experiment in Meta-Evolution Summary and Outlook Appendix A: Data for the Fletcher-Powell Function Appendix B: Data from Selection Experiments Appendix D: The Multiprocessor Environment Appendix E: Mathematical Symbols Bibliography Index
2,866 citations