Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Journal of Global Optimization
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Population set-based global optimization algorithms: some modifications and numerical studies
Computers and Operations Research
A Fuzzy Adaptive Differential Evolution Algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Development of efficient particle swarm optimizers by using concepts from evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Evolutionary approaches for real world applications in 21st century
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Computational Optimization and Applications
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Only a few attempts in past have been made in adopting a unified outlook towards different paradigms in evolutionary computation (EC). The underlying motivation of these studies was aimed at gaining better understanding of evolutionary methods, both at the level of theory as well as application, in order to design efficient evolutionary algorithms for solving wide-range of complex problems. However, the past descriptions have either been too general or sometimes abstract in issuing a clear direction for improving an evolutionary paradigm for a task-specific. This paper recollects the `Unified Theory of Evolutionary Computation' from past and investigates four steps--Initialization, Selection, Generation and Replacement, which are sufficient to describe traditional forms of Evolutionary Optimization Systems such as Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming, Particle Swarm Optimization and differential evolution (DE). Then, a relatively new evolutionary paradigm, DE, is chosen and studied for its performance on a set of unimodal problems. Discovering DEs inability as an efficient solver, DE is reviewed under `Unified Framework' and functional requirements of each step are evaluated. Targeted towards enhancing the DE's performance, several modifications are proposed through borrowing of operations from a benchmark solver G3-PCX. Success of this exercise is demonstrated in a step-by-step fashion via simulation results. The Unified Approach is highly helpful in understanding the role and re-modeling of DE steps in order to efficiently solve unimodal problems. In an avalanching-age of new methods in EC, this study outlines a direction for advancing EC methods by undertaking a collective outlook and an approach of concept-sharing.