Abstract
Problem statement: The purpose of this study was to describe categories of hybrid genetic algorithm and validate that the hybrid genetic algorithm converges to the optimal solution rather than a near optimal solution so that Hybrid Genetic algorithms can be used to solve real world problems and receive significant interest. Approach: We implemented the input allocation problem for a manufacturing unit firstly with pure genetic algorithm using Matlab's GA tool and then compared the results with hybrid genetic algorithm. Results: We observed that the results from applying only pure genetic algorithm to the problem were near optimal whereas when solved using hybrid genetic algorithm the results were significantly better and were optimal. Conclusion: The results presented by pure genetic algorithm and hybrid genetic algorithm are significant and validate that the hybrid genetic algorithm converges to the optimal solution rather than a near optimal solution.
Highlights
Genetic Algorithms (GAs) are search algorithms that are conceptually based on the methods that living organisms adapt to their environment
The above results demonstrates that Genetic Algorithm are able to reach to near optimal solution while if it mixed with any local search i.e., if it is made hybrid genetic algorithm, it converges to the optimal result
We have implemented hybrid genetic algorithm and try to explain how it can improve the efficiency of the given problem and produce an optimal instead of near optimal solution
Summary
Genetic Algorithms (GAs) are search algorithms that are conceptually based on the methods that living organisms adapt to their environment. A new set of string structures is created from the fittest strings from the previous generation and occasionally a randomly altered new part This process of exploiting historical data allows the GA to speculate on new search points and producing better solutions. His research focused on what he called complex adaptive systems Since their development, Genetic Algorithms have been used as optimization tools for complex problems that involve numerous variables or involve combinations of linear and, non-linear equations. The Genetic Algorithm attempts to improve performance leading to an optimal solution In this process, there are two distinct steps (1) the process of improvement and (2) reaching the optimum itself. The genetic algorithm can be visualized as follows: 1. Produce an initial generation of Genomes using a random number generator
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