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Chapter
Multi-objective Optimization: Classical and Evolutionary Approaches
Problems involving multiple conflicting objectives arise in most real world optimization problems. Evolutionary Algorithms (EAs) have gained a wide interest and success in solving problems of this nature for t...
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Chapter
Many-objective Optimization Using Evolutionary Algorithms: A Survey
Multi-objective Evolutionary Algorithms (MOEAs) have proven their effectiveness and efficiency in solving complex problems with two or three objectives. However, recent studies have shown that the performance ...
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Chapter
Practical Applications in Constrained Evolutionary Multi-objective Optimization
Constrained optimization is applicable to most real world engineering science problems. An efficient constraint handling method must be robust, reliable and computationally efficient. However, the performance ...
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Chapter and Conference Paper
Software Anti-patterns Detection Under Uncertainty Using a Possibilistic Evolutionary Approach
Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that could deteriorate the software maintainability and evolution. Despite the high number of existing detection methods, the issu...
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Article
Handling uncertainty in SBSE: a possibilistic evolutionary approach for code smells detection
Code smells, also known as anti-patterns, are poor design or implementation choices that hinder program comprehensibility and maintainability. While several code smell detection methods have been proposed, Man...
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Article
Solving combinatorial bi-level optimization problems using multiple populations and migration schemes
In many decision making cases, we may have a hierarchical situation between different optimization tasks. For instance, in production scheduling, the evaluation of the tasks assignment to a machine requires th...