Open Access
SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
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The article was published on 2002-01-01 and is currently open access. It has received 1972 citations till now. The article focuses on the topics: Pareto principle & Multi-objective optimization.read more
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Recent Developments in Derivative-Free Multiobjective Optimisation
TL;DR: In this article, the basic concepts and ideas commonly considered for the algorithmic development in multiobjective optimization and review some recent classes of methods which do not make use of derivatives are introduced.
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Weighted preferences in evolutionary multi-objective optimization
TL;DR: This work shows how to use a weight distribution function on the objective space to incorporate preference information into hypervolume-based algorithms, and shows that this weighted information can easily be used in other popular EMO algorithms as well.
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The optimality of the standard genetic code assessed by an eight-objective evolutionary algorithm.
TL;DR: The standard genetic code represents most likely only partially optimized systems, which emerged under the influence of many different factors, and can be useful to researchers involved in modifying the genetic code of the living organisms and designing artificial ones.
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A novel multi-objective optimization algorithm based on artificial algae for multi-objective engineering design problems
TL;DR: The results show that MO-AAA efficiently generates the Pareto front and is easy to implement, promising and competitive compared to other state-of-the-art algorithms considered in this work.
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Optimising anti-spam filters with evolutionary algorithms
TL;DR: Two most popular evolutionary multiobjective algorithms and one single objective algorithm are adapted to optimisation of the anti-spam filters' scores and compared on publicly available datasets widely used for benchmarking purposes.