Environmental Adaption Method: A Heuristic Approach for Optimization
Created by W.Langdon from
gp-bibliography.bib Revision:1.8154
- @Article{Chandila:2019:IJAMC,
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author = "Anuj Chandila and Shailesh Tiwari and K. K. Mishra and
Akash Punhani",
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title = "Environmental Adaption Method: A Heuristic Approach
for Optimization",
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journal = "International Journal of Applied Metaheuristic
Computing",
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year = "2019",
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volume = "10",
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number = "1",
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pages = "Article: 7",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1947-8283",
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bibsource = "OAI-PMH server at oai.repec.org",
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identifier = "RePEc:igg:jamc00:v:10:y:2019:i:1:p:107-131",
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oai = "oai:RePEc:igg:jamc00:v:10:y:2019:i:1:p:107-131",
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DOI = "doi:doi=10.4018/IJAMC.2019010107",
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abstract = "This article describes how optimisation is a process
of finding out the best solutions among all available
solutions for a problem. Many randomized algorithms
have been designed to identify optimal solutions in
optimisation problems. Among these algorithms
evolutionary programming, evolutionary strategy,
genetic algorithm, particle swarm optimisation and
genetic programming are widely accepted for the
optimisation problems. Although a number of randomized
algorithms are available in literature for solving
optimisation problems yet their design objectives are
same. Each algorithm has been designed to meet certain
goals like minimising total number of fitness
evaluations to capture nearly optimal solutions, to
capture diverse optimal solutions in multimodal
solutions when needed and also to avoid the local
optimal solution in multi modal problems. This article
discusses a novel optimisation algorithm named as
Environmental Adaption Method (EAM) foable 3r solving
the optimisation problems. EAM is designed to reduce
the overall processing time for retrieving optimal
solution of the problem, to improve the quality of
solutions and particularly to avoid being trapped in
local optima. The results of the proposed algorithm are
compared with the latest version of existing algorithms
such as particle swarm optimisation (PSO-TVAC), and
differential evolution (SADE) on benchmark functions
and the proposed algorithm proves its effectiveness
over the existing algorithms in all the taken cases.",
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notes = "IEC-CET, Greater Noida, India",
- }
Genetic Programming entries for
Anuj Chandila
Shailesh Tiwari
K K Mishra
Akash Punhani
Citations