Results 161 to 170 of about 17,365 (211)
Some of the next articles are maybe not open access.
Clustering with evolution strategies
Pattern Recognition, 1994Tbe applicability of evolution strategies (ESs), population based stochastic optimization techniques, to optimize clustering objective functions is explored. Clustering objective functions are categorized into centroid and non-centroid type of functions.
M Narasimha Murty
exaly +2 more sources
Evolution and Mixed Strategies
Games and Economic Behavior, 2001Following \textit{J. Maynard Smith} [Evolution and the Theory of Games (1982; Zbl 0526.90102)], the authors investigate evolutionary games of the Hawk-Dove type. \textit{R. Selten} [Theory Decis. Libr., Ser. C2, 67-75 (1980; Zbl 0658.90102)] has shown that evolutionary stable strategies in the asymmetric game are always pure strategies. This could mean
Ken Binmore, Larry Samuelson
openaire +1 more source
Applied Intelligence, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kazuhiro Ohkura +2 more
openaire +2 more sources
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kazuhiro Ohkura +2 more
openaire +2 more sources
A differential evolution strategy
2017 IEEE Congress on Evolutionary Computation (CEC), 2017This contribution introduces an evolutionary algorithm (EA) for continuous optimization in ℝn. The algorithm generates new individuals by the standard nonelitist truncation selection and the differential mutation to generate new individuals. The differential mutation is enriched by adding a random vector in the direction of the shift of population ...
Dariusz Jagodzinski, Jaroslaw Arabas
openaire +1 more source
Niching in evolution strategies
Proceedings of the 7th annual conference on Genetic and evolutionary computation, 2005EAs have the tendency to converge quickly into a single solution. Niching methods, the extension of EAs to address this issue, have been investigated up to date mainly within the field of Genetic Algorithms (GAs). In our study we investigate the basis for niching methods within Evolution Strategies (ES), and propose the first ES niching method. Results
Ofer M. Shir, Thomas Bäck
openaire +1 more source
2006 IEEE International Conference on Evolutionary Computation, 2006
We show in this paper that the squared norm of an individual subject to Gaussian mutation in an evolution strategy will grow on average linearly with the number of generations. Although we prove this result in the absence of selection, experimental evidence is provided showing that the result also holds in a full fledged evolution strategy applied to ...
Alejandro Sierra, Alejandro Echeverría
openaire +1 more source
We show in this paper that the squared norm of an individual subject to Gaussian mutation in an evolution strategy will grow on average linearly with the number of generations. Although we prove this result in the absence of selection, experimental evidence is provided showing that the result also holds in a full fledged evolution strategy applied to ...
Alejandro Sierra, Alejandro Echeverría
openaire +1 more source
The evolution of strategy and strategy as evolution
World Futures, 1993Abstract The concept of “strategy” went through a metamorphosic evolution across borders of heritage from its militaristic origin to its development into “strategic management.” Through that evolution, both content and frameworks of strategy changed significantly.
openaire +1 more source
On Interactive Evolution Strategies
2006In this paper we discuss Evolution Strategies within the context of interactive optimization. Different modes of interaction will be classified and compared. A focus will be on the suitability of the approach in cases, where the selection of individuals is done by a human user based on subjective evaluation.
Ron Breukelaar +2 more
openaire +1 more source
An Optimal Strategy of Evolution
The Quarterly Review of Biology, 1974Admissable game-theory models of evolution must be restricted to the class of "existential games" in which there is no way of using the winnings ("payoff") for any purpose other than continuing the game for as long as possible. The optimal strategy in such a game is to minimize the stakes played.
L B, Slobodkin, A, Rapoport
openaire +2 more sources
Contemporary evolution strategies
1995After an outline of the history of evolutionary algorithms, a new (μ, κ, λ, ρ) variant of the evolution strategies is introduced formally. Though not comprising all degrees of freedom, it is richer in the number of features than the meanwhile old (μ, λ) and (μ+λ) versions. Finally, all important theoretically proven facts about evolution strategies are
Hans-Paul Schwefel, Günter Rudolph
openaire +1 more source