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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Hari Mohan Pandey
Amity University
Computer Science & IT Engineering

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Is Parameters Quantification in Genetic Algorithm Important, How to do it? Hari Mohan Pandey
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (513.903 KB) | DOI: 10.11591/ijai.v6.i3.pp112-123

Abstract

The term “appropriate parameters” signifies the correct choice of values has considerable effect on the performance that directs the search process towards the global optima. The performance typically is measured considering both quality of the results obtained and time requires in finding them. A genetic algorithm is a search and optimization technique, whose performance largely depends on various factors – if not tuned appropriately, difficult to get global optima. This paper describes the applicability of orthogonal array and Taguchi approach in tuning the genetic algorithm parameters. The domain of inquiry is grammatical inference has a wide range of applications. The optimal conditions were obtained corresponding to performance and the quality of results with reduced cost and variability. The primary objective of conducting this study is to identify the appropriate parameter setting by which overall performance and quality of results can be enhanced. In addition, a systematic discussion presented will be helpful for researchers in conducting parameters quantification for other algorithms.
Genetic Algorithm for Grammar Induction and Rules Verification through a PDA Simulator Hari Mohan Pandey
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (370.693 KB) | DOI: 10.11591/ijai.v6.i3.pp100-111

Abstract

The focus of this paper is towards developing a grammatical inference system uses a genetic algorithm (GA), has a powerful global exploration capability that can exploit the optimum offspring. The implemented system runs in two phases: first, generation of grammar rules and verification and then applies the GA’s operation to optimize the rules. A pushdown automata simulator has been developed, which parse the training data over the grammar’s rules. An inverted mutation with random mask and then ‘XOR’ operator has been applied introduces diversity in the population, helps the GA not to get trapped at local optimum. Taguchi method has been incorporated to tune the parameters makes the proposed approach more robust, statistically sound and quickly convergent. The performance of the proposed system has been compared with: classical GA, random offspring GA and crowding algorithms. Overall, a grammatical inference system has been developed that employs a PDA simulator for verification.
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