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» Optimization of Genetic Operators for Scheduling Problems
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AIR
1998
103views more  AIR 1998»
14 years 9 months ago
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Abstract. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc...
Francisco Herrera, Manuel Lozano, José L. V...
GECCO
2003
Springer
101views Optimization» more  GECCO 2003»
15 years 2 months ago
An Analysis of a Reordering Operator with Tournament Selection on a GA-Hard Problem
This paper analyzes the performance of a genetic algorithm that utilizes tournament selection, one-point crossover, and a reordering operator. A model is proposed to describe the c...
Ying-Ping Chen, David E. Goldberg
AUSAI
2003
Springer
15 years 2 months ago
Guided Operators for a Hyper-Heuristic Genetic Algorithm
We have recently introduced a hyper-heuristic genetic algorithm (hyper-GA) with an adaptive length chromosome which aims to evolve an ordering of low-level heuristics so as to find...
Limin Han, Graham Kendall
79
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GECCO
2000
Springer
138views Optimization» more  GECCO 2000»
15 years 1 months ago
Domain Knowledge and Representation in Genetic Algorithms for Real World Scheduling Problems
This paper discusses the issues that arise in the design and implementation of an industrialstrength evolutionary-based system for the optimization of the monthly work schedules f...
Ioannis T. Christou, Armand Zakarian
GECCO
2009
Springer
109views Optimization» more  GECCO 2009»
15 years 4 months ago
Crossover operators for multiobjective k-subset selection
Genetic algorithms are often applied to combinatorial optimization problems, the most popular one probably being the traveling salesperson problem. In contrast to permutations use...
Thorsten Meinl, Michael R. Berthold