Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks
نویسندگان
چکیده
This chapter presents a novel framework for tuning the parameters of Evolutionary Algorithms. A hybrid technique combining Meta-EAs and statistical Racing approaches is developed, which is not only capable of effectively exploring the search space of numerical parameters but also suitable for tuning symbolic parameters where it is generally difficult to define any sensible distance metric.
منابع مشابه
Evolutionary Agent-based Policy Analysis in Dynamic Environments Evolutionary Agent-based Policy Analysis in Dynamic Environments
Evolutionary algorithms (EAs) form a rich class of stochastic search methods that use the Darwinian principles of variation and selection to incrementally improve a set of candidate solutions (Eiben and Smith, 2003; Jong, 2006). Both principles can be implemented from a wide variety of components and operators, many with parameters that need to be tuned if the EA is to perform as intended. Tuni...
متن کاملParameter Setting in EAs: a 30 Year Perspective
Parameterized evolutionary algorithms (EAs) have been a standard part of the Evolutionary Computation community from its inception. The widespread use and applicability of EAs is due in part to the ability to adapt an EA to a particular problem-solving context by tuning its parameters. However, tuning EA parameters can itself be a challenging task since EA parameters interact in highly non-line...
متن کاملBeating the 'world champion' evolutionary algorithm via REVAC tuning
We present a case study demonstrating that using the REVAC parameter tuning method we can greatly improve the ‘world champion’ EA (the winner of the CEC2005 competition) with little effort. For ‘normal’ EAs the margins for possible improvements are likely much bigger. Thus, the main message of this paper is that using REVAC great performance improvements are possible for many EAs at moderate co...
متن کاملEvolutionary Algorithm Parameters and Methods to Tune Them
I n this chapter we discuss the notion of Evolutionary Algorithm (EA) parameters and propose a distinction between EAs and EA instances, based on the type of parameters used to specify their details. Furthermore, we consider the most important aspects of the parameter tuning problem and give an overview of existing parameter tuning methods. Finally, we elaborate on the methodological issues inv...
متن کاملPPCea: A Domain-Specific Language for Programmable Parameter Control in Evolutionary Algorithms
An Evolutionary Algorithm (EA) is a meta-heuristic and stochastic optimization search process that mimics Darwinian evolution theory and Mendel's Genetics. Each process facilitates (a) population(s) evolve into fittest and/or convergence by setting parameters of selection, mutation, crossover, population resizing, and/or many other variant operators. However, due to two primary identified facto...
متن کامل