Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Artificial Life
The design and analysis of a computational model of cooperative coevolution
The design and analysis of a computational model of cooperative coevolution
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Computing in Multi-agent Environments: Operators
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
New methods for competitive coevolution
Evolutionary Computation
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Multileveled Symbiotic Evolutionary Algorithm: Application to FMS Loading Problems
Applied Intelligence
Transgenetic algorithm: a new evolutionary perspective for heuristics design
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Computers and Operations Research
Symbiogenesis as a mechanism for building complex adaptive systems: a review
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
A two-leveled symbiotic evolutionary algorithm for clustering problems
Applied Intelligence
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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This paper proposes a new symbiotic evolutionary algorithm to solve complex optimization problems. This algorithm imitates the natural evolution process of endosymbionts, which is called endosymbiotic evolutionary algorithm. Existing symbiotic algorithms take the strategy that the evolution of symbionts is separated from the host. In the natural world, prokaryotic cells that are originally independent organisms are combined into an eukaryotic cell. The basic idea of the proposed algorithm is the incorporation of the evolution of the eukaryotic cells into the existing symbiotic algorithms. In the proposed algorithm, the formation and evolution of the endosymbionts is based on fitness, as it can increase the adaptability of the individuals and the search efficiency. In addition, a localized coevolutionary strategy is employed to maintain the population diversity. Experimental results demonstrate that the proposed algorithm is a promising approach to solving complex problems that are composed of multiple sub- problems interrelated with each other.