The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS
Transactions ID Number: 29-136
Full Name: Shahryar Rahnamayan
Position: Assistant Professor
Age: ON
Sex: Male
Address: 2000 Simcoe Street North, Oshawa, Ontario, Canada L1H 7K4
Country: CANADA
Tel: 905-449-5026
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E-mail address: shahryar.rahnamayan@uoit.ca
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Title of the Paper: Toward Effective Initialization for Large-Scale Search Spaces
Authors as they appear in the Paper: Shahryar Rahnamayan, G. Gary Wang
Email addresses of all the authors: shahryar.rahnamayan@uoit.ca, s2rahnam@engmail.uwaterloo.ca
Number of paper pages: 13
Abstract: Nowadays, optimization problems with a few thousands of variables become more common. Populationbased algorithms, such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Evolutionary Strategies (ES) are commonly used approaches to solve complex large-scale problems from science and engineering. These approaches all work with a population of candidate solutions. On the other hand, for high-dimensional problems, no matter what is the individuals' distribution, the population is highly sparse. Therefore, intelligent employment of individual candidates can play a crucial role to find optimal solution( s) faster. The most majority of population-based algorithms utilize pseudo-random population initialization when there is no a priori knowledge about the solution. In this paper, a center-based population initialization is proposed and investigated on seven benchmark functions. The obtained results are compared with the result!
s of Normal, Pseudo Random, and Latin Hypercube population initialization schemes. Furthermore, the advantages of the proposed center-based sampling method are investigated by a mathematical proof and also Monte Carlo (simulation) method. The detailed experimental verifications are provided for problems with 50, 500, and 1000 dimensions.
Keywords: Population Initialization, Center-Based Sampling, Evolutionary Algorithms, High-Dimensional Search Spaces, Large-Scale Problems.
EXTENSION of the file: .pdf
Special (Invited) Session: Center-Based Initialization for Large-Scale Black-Box Problems
Organizer of the Session: 709-196
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