Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
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
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
Toward a theory of evolution strategies: Some asymptotical results from the (1,+ λ)-theory
Evolutionary Computation
Toward a theory of evolution strategies: The (μ, λ)-theory
Evolutionary Computation
Toward a theory of evolution strategies: On the benefits of sex---the (μ/μ, λ) theory
Evolutionary Computation
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Analyzing the (1, λ) evolution strategy via stochastic approximation methods
Evolutionary Computation
Empirical investigation of multiparent recombination operators in evolution strategies
Evolutionary Computation
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Evolution Strategies (ES) are an approach to numerical optimization that show good optimization performance. The evolutionary behavior of ES has been well-studied on simple problems but not on large complex problems, such as those with highly rugged search spaces, or larger scale problems like those frequently used as benchmark problems for numerical optimization. In this paper, the evolutionary characteristics of ES on complex problems are examined using three different statistical approaches. These cire (1) basic statistical measures at the function-value level, (2) Hotelling's T2 for measuring the balance of exploitation and exploration at the individual-code level and (3) principal components analysis at the individual-code level for visualizing the distribution of the population. Among many formulations of ES, the fast-ES and the robust-ES are adopted for the analyses.