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» Evolutionary Neuroestimation of Fitness Functions
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SAC
2008
ACM
14 years 10 months ago
Dynamic populations in genetic algorithms
Biological populations are dynamic in both space and time, that is, the population size of a species fluctuates across their habitats over time. There are rarely any static or fix...
Zhanshan (Sam) Ma, Axel W. Krings
GECCO
2004
Springer
112views Optimization» more  GECCO 2004»
15 years 3 months ago
Some Issues on the Implementation of Local Search in Evolutionary Multiobjective Optimization
This paper discusses the implementation of local search in evolutionary multiobjective optimization (EMO) algorithms for the design of a simple but powerful memetic EMO algorithm. ...
Hisao Ishibuchi, Kaname Narukawa
GECCO
2003
Springer
132views Optimization» more  GECCO 2003»
15 years 3 months ago
Evolutionary Multimodal Optimization Revisited
Abstract. We revisit a class of multimodal function optimizations using evolutionary algorithms reformulated into a multiobjective framework where previous implementations have nee...
Rajeev Kumar, Peter Rockett
PPSN
1998
Springer
15 years 2 months ago
On Genetic Algorithms and Lindenmayer Systems
This paper describes a system for simulating the evolution of artificial 2D plant morphologies. Virtual plant genotypes are inspired by the mathematical formalism known as Lindenma...
Gabriela Ochoa
79
Voted
GECCO
2006
Springer
174views Optimization» more  GECCO 2006»
15 years 2 months ago
On the analysis of the (1+1) memetic algorithm
Memetic algorithms are evolutionary algorithms incorporating local search to increase exploitation. This hybridization has been fruitful in countless applications. However, theory...
Dirk Sudholt