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75
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GECCO
2007
Springer
187views Optimization» more  GECCO 2007»
15 years 4 months ago
Defining implicit objective functions for design problems
In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examp...
Sean Hanna
GECCO
2007
Springer
163views Optimization» more  GECCO 2007»
15 years 4 months ago
Action-selection and crossover strategies for self-modeling machines
In previous work [7] a computational framework was demonstrated that employs evolutionary algorithms to automatically model a given system. This is accomplished by alternating the...
Josh C. Bongard
76
Voted
GECCO
2007
Springer
176views Optimization» more  GECCO 2007»
15 years 4 months ago
Two-level of nondominated solutions approach to multiobjective particle swarm optimization
In multiobjective particle swarm optimization (MOPSO) methods, selecting the local best and the global best for each particle of the population has a great impact on the convergen...
M. A. Abido
GECCO
2007
Springer
481views Optimization» more  GECCO 2007»
15 years 4 months ago
A hybrid PSO/ACO algorithm for classification
In a previous work we have proposed a hybrid Particle Swarm Optimisation/Ant Colony Optimisation (PSO/ACO) algorithm for the discovery of classification rules, in the context of d...
Nicholas Holden, Alex Alves Freitas
89
Voted
GECCO
2007
Springer
191views Optimization» more  GECCO 2007»
15 years 4 months ago
One-test-at-a-time heuristic search for interaction test suites
Algorithms for the construction of software interaction test suites have focussed on the special case of pairwise coverage; less is known about efficiently constructing test suite...
Renée C. Bryce, Charles J. Colbourn