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GECCO
2009
Springer
103views Optimization» more  GECCO 2009»
13 years 9 months ago
Bumblebees: a multiagent combinatorial optimization algorithm inspired by social insect behaviour
This paper introduces a multiagent optimization algorithm inspired by the collective behavior of social insects. In our method, each agent encodes a possible solution of the probl...
Francesc Comellas, Jesus Martinez-Navarro
CORR
2004
Springer
81views Education» more  CORR 2004»
13 years 11 months ago
Optimizing genetic algorithm strategies for evolving networks
This paper explores the use of genetic algorithms for the design of networks, where the demands on the network fluctuate in time. For varying network constraints, we find the best...
Matthew J. Berryman, Andrew Allison, Derek Abbott
AAAI
1998
14 years 24 days ago
Optimal 2D Model Matching Using a Messy Genetic Algorithm
A Messy Genetic Algorithm is customized toflnd'optimal many-to-many matches for 2D line segment models. The Messy GA is a variant upon the Standard Genetic Algorithm in which...
J. Ross Beveridge
IWANN
2001
Springer
14 years 3 months ago
A New Approach to Evolutionary Computation: Segregative Genetic Algorithms (SEGA)
This paper looks upon the standard genetic algorithm as an artificial self-organizing process. With the purpose to provide concepts that make the algorithm more open for scalabili...
Michael Affenzeller
GECCO
2004
Springer
145views Optimization» more  GECCO 2004»
14 years 4 months ago
An Estimation of Distribution Algorithm Based on Maximum Entropy
Estimation of distribution algorithms (EDA) are similar to genetic algorithms except that they replace crossover and mutation with sampling from an estimated probability distributi...
Alden H. Wright, Riccardo Poli, Christopher R. Ste...
GECCO
2009
Springer
144views Optimization» more  GECCO 2009»
14 years 6 months ago
Cheating for problem solving: a genetic algorithm with social interactions
We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary...
Rafael Lahoz-Beltra, Gabriela Ochoa, Uwe Aickelin