This repository includes implementation of algorithms based on IOHexperimenter. IOHexperimenter is a benchmarking platform for Iterative Optimization Heuristics, which is a part of IOHanalyzer. With data generated by IOHexperimenter, you can easily have performance analyze for your algorithm using IOHanalyzer
- For introduction of IOHexperimenter, please visit https://github.com/IOHprofiler/IOHexperimenter.
- For usage of IOHanalyzer, please visit github page or the online host.
- For details of the IOHanalyzer project, please visit the wiki page.
- For reference of algorithm, please find in the paperBenchmarking discrete optimization heuristics with IOHprofiler, or visit the page.
- Randomized Local Search
- Evolutionary algorithm (EA) with static mutation rate
- Fast genetic algorith (https://dl.acm.org/citation.cfm?id=3071301)
- The two-rate EA with self-adjusting mu-tation rates (https://dl.acm.org/citation.cfm?id=3071279)
- A variant of EA sampling the mutation strength from a normal distribution (https://ieeexplore.ieee.org/abstract/document/8790107)
- The upper EA with an adaptive choice of the variance in the normal distribution from which the mutation strengths are sampled (https://ieeexplore.ieee.org/abstract/document/8790107)
- The EA with log-normal self-adaptation of the mutation rate (https://link.springer.com/chapter/10.1007/3-540-61286-6_141)
- A binary crossover-based EA with self-adjusting population size (https://www.sciencedirect.com/science/article/pii/S0304397514009451).
- "vanilla" genetic algorithm (https://dl.acm.org/citation.cfm?id=229867)
If you have any questions, comments or suggestions, please don't hesitate contacting us IOHprofiler@liacs.leidenuniv.nl!