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» On the Brittleness of Evolutionary Algorithms
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PPSN
2010
Springer
15 years 4 months ago
Negative Drift in Populations
An important step in gaining a better understanding of the stochastic dynamics of evolving populations, is the development of appropriate analytical tools. We present a new drift t...
Per Kristian Lehre
GECCO
2010
Springer
151views Optimization» more  GECCO 2010»
15 years 11 months ago
Sustaining behavioral diversity in NEAT
Niching schemes, which sustain population diversity and let an evolutionary population avoid premature convergence, have been extensively studied in the research field of evoluti...
Hirotaka Moriguchi, Shinichi Honiden
CEC
2009
IEEE
16 years 1 months ago
Dynamic optimization using Self-Adaptive Differential Evolution
Abstract— In this paper we investigate a Self-Adaptive Differential Evolution algorithm (jDE) where F and CR control parameters are self-adapted and a multi-population method wit...
Janez Brest, Ales Zamuda, Borko Boskovic, Mirjam S...
GECCO
2004
Springer
15 years 12 months ago
Upper Bounds on the Time and Space Complexity of Optimizing Additively Separable Functions
Abstract. We present upper bounds on the time and space complexity of finding the global optimum of additively separable functions, a class of functions that has been studied exten...
Matthew J. Streeter
GECCO
2003
Springer
130views Optimization» more  GECCO 2003»
15 years 11 months ago
A New Approach to Improve Particle Swarm Optimization
Abstract. Particle swarm optimization (PSO) is a new evolutionary computation technique. Although PSO algorithm possesses many attractive properties, the methods of selecting inert...
Liping Zhang, Huanjun Yu, Shangxu Hu