scholarly journals Adaptation of Parametric Uniform Crossover in Genetic Algorithm

2013 ◽  
Author(s):  
Farhad Nadi ◽  
Ahamad Tajudin Khader
Keyword(s):  
Genetic Algorithm ◽  
Uniform Crossover

scholarly journals PROBABILISTIC ADAPTIVE CROSSOVER (PAX): A NOVEL GENETIC ALGORITHM CROSSOVER METHODOLOGY

2008 ◽  
Vol 17 (06) ◽  
pp. 1131-1160 ◽  
Author(s):  
SEBASTIÁN A. SALAH ◽  
MANUEL A. DUARTE-MERMOUD ◽  
NICOLÁS H. BELTRÁN
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Probability Distribution ◽  
Test Problems ◽  
Probability Vector ◽  
Uniform Crossover ◽  
Royal Road ◽  
Adaptive Crossover

A new crossover technique for genetic algorithms is proposed in this paper. The technique is called probabilistic adaptive crossover and denoted by PAX. The method includes the estimation of the probability distribution of the population, in order to store in a unique probability vector P information about the best and the worse solutions of the problem to be solved. The proposed methodology is compared with six crossover techniques namely: one-point crossover, two-point crossover, SANUX, discrete crossover, uniform crossover and selective crossover. These methodologies were simulated and compared over five test problems described by ONEMAX Function, Royal Road Function, Random L-MaxSAT, Bohachevsky Function, and the Himmelblau Function.

scholarly journals Evaluation of spatially variable control parameters in a complex catchment modelling system: a genetic algorithm application

2007 ◽  
Vol 9 (3) ◽  
pp. 163-173 ◽  
Author(s):  
Tianjun Fang ◽  
James E. Ball
Keyword(s):  
Genetic Algorithm ◽  
Careful Consideration ◽  
Successful Implementation ◽  
Control Parameters ◽  
Catchment Modelling ◽  
Uniform Crossover ◽  
Variable Control ◽  
Real Value ◽  
Value Coding ◽  
Modelling System

Successful implementation of a catchment modelling system requires careful consideration of the system calibration which involves evaluation of many spatially and temporally variable control parameters. Evaluation of spatially variable control parameters has been an issue of increasing concern arising from an increased awareness of the inappropriateness of assuming catchment averaged values. Presented herein is the application of a real-value coding genetic algorithm (GA) for evaluation of spatially variable control parameters for implementation with the Storm Water Management Model (SWMM). It was found that a real-value coding GA using multiple storms calibration was a robust search technique that was capable of identifying the most promising range of values for spatially variable control parameters. As the selection of appropriate GA operators is an important aspect of the GA efficiency, a comprehensive investigation of the GA operators in a high-dimensional search space was conducted. It was found that a uniform crossover operation was superior to both one-point and two-point crossover operations over the whole range of crossover probabilities, and the optimal uniform crossover and mutation probabilities for the complex system considered were in the range of 0.75–0.90 and 0.01–0.1, respectively.

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