Titlebook: Evolutionary Multi-Criterion Optimization; 8th International Co António Gaspar-Cunha,Carlos Henggeler Antunes,Carl Conference proceedings 2

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Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmarks derived. It is shown which contributions were made to which phase of the MBMO process. A special attention is given to the proposal of a set of points for parallel evaluation within a batch. Proposals for four different MBMO algorithms are presented and compared to their sequential variants within

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Temporal Innovization: Evolution of Design Principles Using Multi-objective Optimizationsolutions lie on the Pareto-optimal front which is a lower-dimensional slice of the objective space. Together, the solutions may possess special properties that make them optimal over other feasible solutions. Innovization is the process of extracting such special properties (or design principles) f

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Truculent

Using Hyper-Heuristic to Select Leader and Archiving Methods for Many-Objective Problemsed different leader and archiving methods to tackle the challenges caused by the increase in the number of objectives, however, selecting the most appropriate components for a given problem is not a trivial task. Moreover, the algorithm can take advantage by using a variety of methods in different p

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Experiments on Local Search for Bi-objective Unconstrained Binary Quadratic Programmingic programming problems. First, we investigate two scalarizing strategies that iteratively identify a high-quality solution for a sequence of sub-problems. Each sub-problem is based on a static or adaptive definition of weighted-sum aggregation coefficients, and is addressed by means of a state-of-t

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