19 research outputs found
An Experimental Study of Adaptive Control for Evolutionary Algorithms
The balance of exploration versus exploitation (EvE) is a key issue on
evolutionary computation. In this paper we will investigate how an adaptive
controller aimed to perform Operator Selection can be used to dynamically
manage the EvE balance required by the search, showing that the search
strategies determined by this control paradigm lead to an improvement of
solution quality found by the evolutionary algorithm
Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates
We analyze the performance of the 2-rate Evolutionary Algorithm
(EA) with self-adjusting mutation rate control, its 3-rate counterpart, and a
~EA variant using multiplicative update rules on the OneMax
problem. We compare their efficiency for offspring population sizes ranging up
to and problem sizes up to .
Our empirical results show that the ranking of the algorithms is very
consistent across all tested dimensions, but strongly depends on the population
size. While for small values of the 2-rate EA performs best, the
multiplicative updates become superior for starting for some threshold value of
between 50 and 100. Interestingly, for population sizes around 50,
the ~EA with static mutation rates performs on par with the best
of the self-adjusting algorithms.
We also consider how the lower bound for the mutation rate
influences the efficiency of the algorithms. We observe that for the 2-rate EA
and the EA with multiplicative update rules the more generous bound
gives better results than when is
small. For both algorithms the situation reverses for large~.Comment: To appear at Genetic and Evolutionary Computation Conference
(GECCO'19). v2: minor language revisio
An Empirical Study of Off-line Configuration and On-line Adaptation in Operator Selection
Automating the process of finding good parameter settings is important in the design of high-performing algorithms. These automatic processes can generally be categorized into off-line and on-line methods. Off-line configuration consists in learning and selecting the best setting in a training phase, and usually fixes it while solving an instance. On-line adaptation methods on the contrary vary the parameter setting adaptively during each algorithm run. In this work, we provide an empirical study of both approaches on the operator selection problem, explore the possibility of varying parameter value by a non-adaptive distribution tuned off-line, and incorporate the off-line with on-line approaches. In particular, using an off-line tuned distribution to vary parameter values at runtime appears to be a promising idea for automatic configuration. © 2014 Springer International Publishing.SCOPUS: cp.kinfo:eu-repo/semantics/publishe
An Empirical Study of Meta- and Hyper-Heuristic Search for Multi-Objective Release Planning
A variety of meta-heuristic search algorithms have been introduced for optimising software release planning. However, there has been no comprehensive empirical study of different search algorithms across multiple different real-world datasets. In this article, we present an empirical study of global, local, and hybrid meta- and hyper-heuristic search-based algorithms on 10 real-world datasets. We find that the hyper-heuristics are particularly effective. For example, the hyper-heuristic genetic algorithm significantly outperformed the other six approaches (and with high effect size) for solution quality 85% of the time, and was also faster than all others 70% of the time. Furthermore, correlation analysis reveals that it scales well as the number of requirements increases