机器学习
遗传算法 (GAs) 提供了一种受生物进化启发的学习方法。与从一般到特殊或从简单到复杂的假设搜索不同,遗传算法通过反复变异和重组当前已知最佳假设的部分来生成后续假设。
在每一步中,一个被称为当前种群的假设集合会得到更新,其中一部分种群被当前最适应假设的后代所取代。这个过程形成了一种生成-测试的束搜索(beam-search)假设,其中最佳当前假设的变体最有可能在下一步被考虑。遗传算法的流行得益于多种因素,包括:
- 进化被认为是生物系统内一种成功且鲁棒的适应方法。
- 遗传算法可以搜索包含复杂交互部分的假设空间,在这种空间中,每个部分对整体假设适应度的影响可能难以建模。
- 遗传算法易于并行化,并且可以利用强大计算机硬件成本不断下降的优势。
Genetic algorithms (GAS) provide a learning method motivated by an analogy to biological
evolution. Rather than search from general-to-specific hypotheses, or from simple-to-complex, GAS
generate successor hypotheses by repeatedly mutating and recombining parts of the best currently
known hypotheses. At each step, a collection of hypotheses called the current population is updated by
replacing some fraction of the population by offspring of the most fit current hypotheses. The process
forms a generate-and-test beam-search of hypotheses, in which variants of the best current hypotheses
are most likely to be considered next. The popularity of GAS is motivated by a number of factors
including:
Evolution is known to be a successful, robust method for adaptation within biological systems.
GAS can search spaces of hypotheses containing complex interacting parts, where the impact of
each part on overall hypothesis fitness may be difficult to model.
Genetic algorithms are easily parallelized and can take advantage of the decreasing costs of
powerful computer hardware.