Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the Convergence of Pattern Search Algorithms
SIAM Journal on Optimization
Genetic engineering versus natural evolution: genetic algorithms with deterministic operators
Journal of Systems Architecture: the EUROMICRO Journal
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Evolutionary Computation
ALPS: the age-layered population structure for reducing the problem of premature convergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Population sizing for entropy-based model building in discrete estimation of distribution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Benchmarking evolutionary and hybrid algorithms using randomized self-similar landscapes
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
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Many current Evolutionary Algorithms suffer from a tendency to prematurely lose their capability to incorporate new genetic material, resulting in a stagnation in suboptimal points. To successfully apply these methods on increasingly complex problems, the ability to generate useful variations leading to continuous improvements is vital. Nevertheless, there is a major difficulty in finding computational extensions to the evolutionary paradigm that ensures a continuous emergence of new qualitative solutions, as the essence of the Darwinian paradigm - the natural selection - acts as a stabilizing force, keeping the population into an evolutionary equilibria. It is suggested that replacing the survival of the fittest paradigm with a cooperative framework, where individuals are highly specialized on different exploring and exploitive strategies, results in a highly efficient, non-convergent, sustainable search process, where new optima emerge continually. Proposed technique is validated on the test suits of CEC'08 Large Scale Optimization Contest.