Abstract: The No Free Lunch (NFL) theorem for search and optimisation states that averaged across all possible objective functions on a fixed search space, all search algorithms perform equally well. Several refined versions of the theorem find a similar outcome when averaging across smaller sets of functions. This paper argues that NFL results continue to be misunderstood by many researchers, and addresses this issue in several ways. Existing arguments against real-world implications of NFL results are collected and re-stated for accessibility and new ones are added. Specific misunderstandings extant in the literature are identified, with speculation as to how they may have arisen. This paper presents an argument against a common paraphrase of NFL findings—that algorithms must be specialised to problem domains to do well—after problematising the usually undefined term “domain”. It provides novel concrete counter-examples illustrating cases where NFL theorems do not apply. In conclusion, it offers a novel view of the real meaning of NFL, incorporating the anthropic principle and justifying the position that in many common situations researchers can ignore NFL. PubDate: 2019-03-25 DOI: 10.1007/s42257-019-00002-6
Abstract: In this work, a fuzzy method for dynamic adjustment of parameters in galactic swarm optimization is presented. Galactic swarm optimization is based on the movement of stars and galaxies in the universe, as well as their attractive influence allowing the use of multiple cycles of exploration and exploitation to solve complex optimization problems. It has been observed in the literature that the utilization of fuzzy systems for dynamic adjustment of parameters in metaheuristic algorithms produces good results when compared to using fixed parameters in the algorithms. In this work, the adjustment of the c3 and c4 parameters is made through the use of fuzzy systems because these parameters have a significant role in the operation of galactic swarm optimization. We tested the fuzzy approach with a set of benchmark mathematical functions and with the fuzzy controller of the water tank problem to measure the performance. Finally, a comparison of the results is presented among the proposed method and other metaheuristics. PubDate: 2018-12-14 DOI: 10.1007/s42257-018-0001-9
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