When and Why Metaheuristics Researchers can Ignore xe2x80x9cNo Free Lunchxe2x80x9d Theorems 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 realworld implications of NFL results are collected and restated 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 findingsxe2x80x94that algorithms must be specialised to problem domains to do wellxe2x80x94after problematising the usually undefined term xe2x80x9cdomainxe2x80x9d. It provides novel concrete counterexamples 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.

Benchmarking discrete optimization heuristics with IOHprofiler Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems. Such comparisons carry the potential to provide insights into the strengths and weaknesses of different approaches, which can be leveraged into designing new algorithms. Carefully selected benchmark problems are also needed as training sets in the context of algorithm selection and configuration. With the ultimate goal to create a meaningful benchmark set for iterative optimization heuristics, we compile and assess in this work a selection of discrete optimization problems that subscribe to different types of fitness landscapes. All problems have been implemented and tested within IOHprofiler, our recently released software built to assess iterative heuristics solving combinatorial optimization problems. For each selected problem we compare performances of eleven different heuristics. Apart from fixedtarget and fixedbudget results for the individual problems, we also derive ECDF results for groups of problems. To obtain these, we have implemented an addon for IOHprofiler which allows aggregation of performance data across different benchmark functions.