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  • Influence
The balance between proximity and diversity in multiobjective evolutionary algorithms
TLDR
In this paper, we argue that the development of new MOEAs cannot converge onto a single new most efficient MOEA because the performance of MOE as shows characteristics of multiobjective problems. Expand
  • 468
  • 83
  • PDF
An adaptive pursuit strategy for allocating operator probabilities
  • D. Thierens
  • Computer Science, Mathematics
  • GECCO '05
  • 25 June 2005
TLDR
Learning the optimal probabilities of applying an exploration operator from a set of alternatives can be done by self-adaptation or by adaptive allocation rules. Expand
  • 205
  • 25
  • PDF
Scalability Problems of Simple Genetic Algorithms
  • D. Thierens
  • Mathematics, Medicine
  • Evolutionary Computation
  • 1 December 1999
TLDR
We show that there is a way to solve, at least in principle, fully deceptive functions by using uniform crossover and a high selection pressure. Expand
  • 130
  • 13
  • PDF
The Linkage Tree Genetic Algorithm
  • D. Thierens
  • Mathematics, Computer Science
  • PPSN
  • 11 September 2010
TLDR
We introduce the Linkage Tree Genetic Algorithm (LTGA), a competent genetic algorithm that learns the linkage between the problem variables without knowing the actual position of the linked variables. Expand
  • 63
  • 12
Mixing in Genetic Algorithms
  • 274
  • 11
Optimal mixing evolutionary algorithms
TLDR
A key search mechanism in Evolutionary Algorithms is the mixing or juxtaposing of partial solutions present in the parent solutions. Expand
  • 79
  • 11
  • PDF
Adaptive mutation rate control schemes in genetic algorithms
  • D. Thierens
  • Mathematics
  • Proceedings of the Congress on Evolutionary…
  • 12 May 2002
TLDR
We propose two simple adaptive mutation rate control schemes, and show their feasibility in comparison with a fixed mutation rate, a self-adaptive mutation rate and a deterministically scheduled dynamic mutation rate. Expand
  • 151
  • 10
  • PDF
Continuous iterated density estimation evolutionary algorithms within the IDEA framework
In this paper, we formalize the notion of performing optimization by iterated density estimation evolutionary algorithms as the IDEA framework. These algorithms build probabilistic models andExpand
  • 119
  • 10
  • PDF
Convergence Models of Genetic Algorithm Selection Schemes
TLDR
We discuss the use of normal distribution theory as a tool to model the convergence characteristics of different GA selection schemes. Expand
  • 204
  • 9
  • PDF
Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA
TLDR
We formalize the notion of building and using probabilistic models in a new framework named IDEA. Expand
  • 144
  • 9
  • PDF
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