A fast computation of the hypervolume has become a crucial component for the quality assessment and the performance of modern multi-objective evolutionary optimization algorithms. Albeit recent improvements, exact computation becomes quickly infeasible if the optimization problems scale in their number of objectives or size. To overcome this issue, we investigate the potential of using approximation instead of exact computation by benchmarking the state of the art hypervolume algorithms for different geometries, dimensionality and number of points. Our experiments outline the threshold at which exact computation starts to become infeasible, but approximation still applies, highlighting the major factors that influence its performance.
Meta-heuristics have proven to be an efficient method of handling difficult global optimization tasks. A recent trend in evolutionary computation is the use of several meta-heuristics at the same time, allowing for occasional information exchange among them in hope to take advantage from the best algorithmic properties of all. Such an approach is inherently parallel and, with some restrictions, has a straight forward implementation in a heterogeneous island model. We propose a methodology for characterizing the interplay between different algorithms, and we use it to discuss their performance on real-parameter single objective optimization benchmarks. We introduce the new concepts of feedback, saturation and injection, and show how they are powerful tools to describe the interplay between different algorithms and thus to improve our understanding of the internal mechanism at work in large parallel evolutionary set-ups.
Traditionally aging research focused on the disintegration of social ties, however it has recently been observed that whom we contact has a larger impact on well-being
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