Special Issue "Evolutionary Computation"
A special issue of Mathematics (ISSN 2227-7390).
Deadline for manuscript submissions: closed (31 March 2019).
A printed edition of this Special Issue is available here.
Interests: evolutionary computation; swarm intelligence; metaheuristics; fuzzy scheduling; big data optimization; multi-objective and many-objective optimization
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Evolutionary computation (EC) is a family of algorithms for global optimization inspired by biological evolution. It includes various population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In EC, each individual has a simple structure and function. EC system is composed of many of these individuals and can address difficult real-world problems, which are impossible to be solved by single individuals. During the recent decades, the EC methods have been successfully applied to solve complex and time-consuming problems. The EC is indeed a topic of interest amongst researchers in various fields of science and engineering. Some of the most popular EC paradigms are genetic algorithm, genetic programming, and evolution strategy. Many theoretical and experimental studies have proved the significant properties of EC such as reasoning with vague and/or ambiguous data, adaptation to dynamic and uncertain environments, and learning from noisy and/or incomplete information.The aim of this special issue is to compile the latest theory and applications in the field of EC. Submissions should be original and unpublished, and present novel in-depth fundamental research contributions either from a methodological perspective or from an application point of view. In general, we are soliciting contributions on (but not only limited to) the following topics:
- Improvements of traditional EC methods (e.g., genetic algorithm, differential evolution, ant colony optimization and particle swarm optimization)
- Recent development of EC methods (e.g., biogeography-based optimization, krill herd (KH) algorithm, monarch butterfly optimization (MBO), earthworm optimization algorithm (EWA), elephant herding optimization (EHO), moth search (MS) algorithm, rhino herd (RH) algorithm)
- Theoretical study on EC algorithms using various techniques (e.g., Markov chain, dynamic system, complex system/networks, and Martingale)
- Application of EC methods (e.g., scheduling, data mining, machine learning, reliability, planning, task assignment problem, IIR filter design, traveling salesman problem, optimization under dynamic and uncertain environments)
Dr. Gai-Ge Wang
Dr. Amir H. Alavi
Manuscript Submission Information
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