The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Journal of Global Optimization
The Stud GA: A Mini Revolution?
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Genetic Algorithms and Evolution Strategies - Similarities and Differences
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Architecture for an Artificial Immune System
Evolutionary Computation
Evolving Evolutionary Algorithms with Patterns
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Journal of Global Optimization
Toward a theory of evolution strategies: The (μ, λ)-theory
Evolutionary Computation
Covariance Matrix Adaptation Revisited --- The CMSA Evolution Strategy ---
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Oppositional biogeography-based optimization
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
An analysis of the equilibrium of migration models for biogeography-based optimization
Information Sciences: an International Journal
Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms
Information Sciences: an International Journal
Variants of Evolutionary Algorithms for Real-World Applications
Variants of Evolutionary Algorithms for Real-World Applications
Self-adaptive differential evolution with multi-trajectory search for large-scale optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Evolutionary algorithms (EAs) are fast and robust computation methods for global optimization, and have been widely used in many real-world applications. We first conceptually discuss the equivalences of various popular EAs including genetic algorithm (GA), biogeography-based optimization (BBO), differential evolution (DE), evolution strategy (ES) and particle swarm optimization (PSO). We find that the basic versions of BBO, DE, ES and PSO are equal to the GA with global uniform recombination (GA/GUR) under certain conditions. Then we discuss their differences based on biological motivations and implementation details, and point out that their distinctions enhance the diversity of EA research and applications. To further study the characteristics of various EAs, we compare the basic versions and advanced versions of GA, BBO, DE, ES and PSO to explore their optimization ability on a set of real-world continuous optimization problems. Empirical results show that among the basic versions of the algorithms, BBO performs best on the benchmarks that we studied. Among the advanced versions of the algorithms, DE and ES perform best on the benchmarks that we studied. However, our main conclusion is that the conceptual equivalence of the algorithms is supported by the fact that algorithmic modifications result in very different performance levels.