Crossover Strategy for Improved Solution Space Exploration With Genetic Algorithms

1998 ◽  
Author(s):  
Kurt S. Anderson ◽  
YuHung Hsu
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Single Point ◽  
Solution Space ◽  
Nonuniform Distribution ◽  
High Dimensional ◽  
Optimal Solutions ◽  
Crossover Operator ◽  
Entire Chromosome ◽  
Dimensional Optimization

Abstract The following paper presents a modified crossover operator to extend the exploration capability in Genetic Algorithms for high dimensional optimization problems. Traditional strategies apply crossover once on a pair of selected chromosomes to generate two offspring by randomly selecting a single crossover location within the chromosomal length. The proposed method applies crossover once on each separate gene (variable) instead of on the entire chromosome. To further accelerate exploration of the Genetic Algorithm, nonuniform distribution of the respective crossover position on each gene has also been studied. The empirical results show that Genetic Algorithms with the proposed crossover strategies can find optimal or near optimal solutions within fewer generations than traditional single point crossover.

Space Contraction and Partition Algorithm for Combinatorial Optimization

2011 ◽  
Vol 421 ◽  
pp. 559-563
Author(s):  
Yong Chao Gao ◽  
Li Mei Liu ◽  
Heng Qian ◽  
Ding Wang
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Solution Space ◽  
Search Space ◽  
Small Scale ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Intelligent Algorithms ◽  
Combinatorial Optimization Problems

The scale and complexity of search space are important factors deciding the solving difficulty of an optimization problem. The information of solution space may lead searching to optimal solutions. Based on this, an algorithm for combinatorial optimization is proposed. This algorithm makes use of the good solutions found by intelligent algorithms, contracts the search space and partitions it into one or several optimal regions by backbones of combinatorial optimization solutions. And optimization of small-scale problems is carried out in optimal regions. Statistical analysis is not necessary before or through the solving process in this algorithm, and solution information is used to estimate the landscape of search space, which enhances the speed of solving and solution quality. The algorithm breaks a new path for solving combinatorial optimization problems, and the results of experiments also testify its efficiency.

Improved Embedded Micro-PSO for High-Dimensional Optimization Problems

2012 ◽  
Vol 236-237 ◽  
pp. 1184-1189
Author(s):  
Wen Hua Han ◽  
Chang Dong Zhu
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Small Population ◽  
High Dimensional ◽  
Adaptive Mutation ◽  
Large Scale Optimization ◽  
Continuous Variables ◽  
Scale Optimization ◽  
Dimensional Optimization

This paper presents a novel optimization technique called embedded micro-particle swarm optimization (EMPSO) to solve high-dimensional problems with continuous variables. The proposed EMPSO adopts a population memory which is divided into two portions as the source of diversity, and an external memory to collect particles performing well in an embedded PSO with a very small population size. However, the fact that the new method doesn’t excel in all of the benchmark functions highlights the necessity of developing improvement. Thus an adaptive mutation operator is introduced into EMPSO to remedy the issue. The experimental results show that the improved EMPSO has good performance for solving large-scale optimization problems.

RDF Query Path Optimization Using Hybrid Genetic Algorithms

2022 ◽  
Vol 12 (1) ◽  
pp. 1-16
Author(s):  
Qazi Mudassar Ilyas ◽  
Muneer Ahmad ◽  
Sonia Rauf ◽  
Danish Irfan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Execution Time ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Solution Space ◽  
Path Optimization ◽  
Initial Population ◽  
Description Framework ◽  
The Cost

Resource Description Framework (RDF) inherently supports data mergers from various resources into a single federated graph that can become very large even for an application of modest size. This results in severe performance degradation in the execution of RDF queries. As every RDF query essentially traverses a graph to find the output of the Query, an efficient path traversal reduces the execution time of RDF queries. Hence, query path optimization is required to reduce the execution time as well as the cost of a query. Query path optimization is an NP-hard problem that cannot be solved in polynomial time. Genetic algorithms have proven to be very useful in optimization problems. We propose a hybrid genetic algorithm for query path optimization. The proposed algorithm selects an initial population using iterative improvement thus reducing the initial solution space for the genetic algorithm. The proposed algorithm makes significant improvements in the overall performance. We show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost.

A Novel Modified Tree-Seed Algorithm for High-Dimensional Optimization Problems

2020 ◽  
Vol 29 (2) ◽  
pp. 337-343 ◽  
Author(s):  
Shijie Zhao ◽  
Leifu Gao ◽  
Jun Tu ◽  
Dongmei Yu
Keyword(s):  
Optimization Problems ◽  
High Dimensional ◽  
Dimensional Optimization ◽  
Tree Seed
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