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» Adapting binary fitness functions in Genetic Algorithms
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GECCO
2003
Springer
155views Optimization» more  GECCO 2003»
15 years 5 months ago
Adaptive Elitist-Population Based Genetic Algorithm for Multimodal Function Optimization
Abstract. This paper introduces a new technique called adaptive elitistpopulation search method for allowing unimodal function optimization methods to be extended to efficiently lo...
Kwong-Sak Leung, Yong Liang
GECCO
2005
Springer
131views Optimization» more  GECCO 2005»
15 years 5 months ago
EA models and population fixed-points versus mutation rates for functions of unitation
Using a dynamic systems model for the Simple Genetic Algorithm due to Vose[1], we analyze the fixed point behavior of the model without crossover applied to functions of unitation...
J. Neal Richter, John Paxton, Alden H. Wright
CEC
2007
IEEE
15 years 1 months ago
A novel general framework for evolutionary optimization: Adaptive fuzzy fitness granulation
— Computational complexity is a major challenge in evolutionary algorithms due to their need for repeated fitness function evaluations. Here, we aim to reduce number of fitness f...
Mohsen Davarynejad, Mohammad R. Akbarzadeh-Totonch...
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GECCO
2008
Springer
141views Optimization» more  GECCO 2008»
15 years 25 days ago
Potential fitness for genetic programming
We introduce potential fitness, a variant of fitness function that operates in the space of schemata and is applicable to tree-based genetic programing. The proposed evaluation ...
Krzysztof Krawiec, PrzemysBaw Polewski
ITICSE
1997
ACM
15 years 4 months ago
A genetic algorithms tutorial tool for numerical function optimisation
The field of Genetic Algorithms has grown into a huge area over the last few years. Genetic Algorithms are adaptive methods, which can be used to solve search and optimisation pro...
Edmund K. Burke, D. B. Varley