Analyzing the statistical features of CIXL2 crossover offspring

  title={Analyzing the statistical features of CIXL2 crossover offspring},
  author={C{\'e}sar Herv{\'a}s‐Mart{\'i}nez and Domingo Ortiz-Boyer},
  journal={Soft Computing},
We cannot deny the effort that the scientific community is devoting to the explanation of the features of the crossover operator of real-coded genetic algorithms and its effect over the evolutive process. This paper is another step in that direction, we analyze the behavior of the Confidence Interval Based Crossover using L2 Norm (CIXL2). This crossover is based on the learning of the statistical features of localization and dispersion of the best individuals of the population. The crossover… 
CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features
A crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions that takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring.
Robust confidence intervals applied to crossover operator for real-coded genetic algorithms
This work proposes a new approach to crossover operators for real-coded genetic algorithms based on robust confidence intervals, an alternative to standard confidence intervals used for localising the search regions where the best individuals are placed.
Real-parameter crossover operators with multiple descendents: An experimental study
The experimental results obtained confirm that the generation of multiple descendents along with the offspring selection mechanism that chooses the two best offspring may enhance the operation of these three crossover operators, and are more efficient than standard real-coded genetic algorithms, that is, they offer solutions with higher quality, requiring fewer fitness function evaluations.
A Modified Real-Coded Extended Line Crossover for Genetic Algorithm
A modified form of extended line crossover (m-RCELX) is proposed which is simple and efficient to solve the optimization problems especially the problems whose optimality lies at the boundary of its domain.
Improving crossover operator for real-coded genetic algorithms using virtual parents
This work proposes a new strategy to improve the performance of this crossover operator by the creation of virtual parents obtained from the population parameters of localisation and dispersion of the best individuals.
Crossover effect over penalty methods in function optimization with constraints
This work makes an analysis of the influence of the crossover operator in this kind of problems and uses a test set that includes functions with linear and nonlinear constraints.
Directed intervention crossover approaches in genetic algorithms with application to optimal control problems
This thesis presents extensions to the GA crossover process, termed directed intervention crossover techniques, that greatly reduce the number of fitness function evaluations required to find fit solutions, thus increasing the effectiveness of GAs for problems of this type.
Real‐parameter crossover operators with multiple descendents: An experimental study
There has been an increasing interest in incorporating this crossover scheme into real‐coded genetic algorithm models because its operation was particularly suitable to attain reliable and accurate solutions for many continuous optimization problems.
GAAP. genetic algorithm with auxiliary populations applied to continuous optimization problems
A method that combines multiple auxiliary populations with the main population of the algorithm is proposed to prevent or hinder the early convergence to local suboptimal solutions and to provide a local search mechanism for a greater exploitation of the most promising regions within the search space.


Theoretical analysis of the unimodal normal distribution crossover for real-coded genetic algorithms
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  • Computer Science
    1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360)
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The importance of the distribution and statistics of the offspring yielded by a crossover operator for its evaluation is discussed and the unimodal normal distribution crossover (UNDX) developed by Ono et al. (1997) is analyzed.
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An extension of evolution strategies to multiparent recombination involving a variable number of parents to create an offspring individual is proposed. The extension is experimentally evaluated on a
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    Evolutionary Computation
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