Applications of Multi-Objective Evolutionary Algorithms

Dear Colleagues,

Multi-objective evolutionary algorithms (MOEAs) are population-based metaheuristics that allow us to find, in a single run, multiple Pareto solutions for multi-objective problems. This property allows the decision maker to choose, in an "a posteriori" decision process, the most satisfactory solution according to the current decision environment. In this way, if the decision environment changes, it is not necessary to run the algorithm again, but simply to choose another solution from the set of Pareto solutions. MOEAs are very powerful techniques that have been applied successfully in numerous applications and multiple types of optimization, search and machine learning problems.

This Special Issue invites original research papers that report on the state-of-the-art and recent advancements in “Applications of Multi-Objective Evolutionary Algorithms”. The scope of this Special Issue encompasses applications in engineering, artificial intelligence, physical science, social science, business, economy, market research, and medical and health care. Topics of interest include (but are not limited to) the following subject categories:

• Applications of MOEAs to multi-objective constrained optimization problems.
• Applications of MOEAs to combinatorial optimization problems.
• Applications of MOEAs to integer and mixed optimization problems.
• Applications of MOEAs in fuzzy optimization.
• Applications of multi-objective differential evolution algorithms.
• Applications of multi-objective genetic programming algorithms.
• Applications of MOEAs to many-objective optimization.
• Applications of MOEAS to search problems.
• Applications of MOEAs in machine learning: feature selection, classification, clustering, association rules, regression, time series forecasting, deep learning, data mining, big data.
• Applications of parallel and distributed MOEAs.

Dr. Fernando Jiménez
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Evolutionary algorithms
• Multi-objective optimization
• Search
• Machine learning
• Real life applications