New Ways to Calibrate Evolutionary Algorithms

Abstract

Summary. The issue of setting the values of various parameters of an evolutionary algorithm (EA) is crucial for good performance. One way to do it is controlling EA parameters on-the-fly, which can be done in various ways and for various parameters. We briefly review these options in general and present the findings of a literature search and some statistics about the most popular options. Thereafter, we provide three case studies indicating a high potential for uncommon variants. In particular, we recommend to focus on parameters regulating selection and population size, rather than those concerning crossover and mutation. On the technical side, the case study on adjusting tournament size shows by example that global parameters can also be self-adapted, and that heuristic adaptation and pure self-adaptation can be successfully combined into a hybrid of the two.

The use of differential evolution algorithm for solving chemical engineering problems

“…Due to their influences, finding the optimal values of the control parameters is not a straightforward process, and the methods used for their correct settings can be classified into parameter tuning and parameter control (Eiben and Schut 2008). Parameter tuning consists of finding good values before running the algorithm.…”

“…On the other hand, in the case of parameter control, the values are changed dynamically during the run based on a set of defined rules. Taking into account the "how" criterion of Eiben and Schut (2008) indicating how the change is performed, four subclasses are encountered: (i) deterministic control, where the parameters are determined using a deterministic law, without feedback from the system; (ii) adaptive control, where the parameters are determined using feedback from the system. Two categories are encountered here: parameter adaptation, which obeys the state of the population and refresh of population; (iii) self-adaptive control, where the parameters are dependent of the algorithm being encoded into it; and (iv) hybrid control.…”

An Adaptive GA in Partitioned Search Space

“…The compared algorithms are Compact GA (cGA), [12] the PGA , [23] GASAT [9], GAHSAT [9], hand-tuned [9], meta-GA [6] and REVAC [6]. It has to be noted that the hand-tuned algorithm here is a canonical GA on which the values of the parameters are carefully tuned.…”

Energy-Efficient Computing Using Agent-Based Multi-objective Dynamic Optimization

“…The chapter identifies the main components one deals with in energy-efficient autonomic computing and, through abstraction and conceptualization, advances the idea of a generic application framework. Evolutionary Algorithms (EAs) represent a natural option to consider given their capability of dealing with highly multi-modal functions in both mono and multi-objective cases as well as their notorious success for various applications [7,13]. Furthermore, the adoption of an agent oriented paradigm is clearly adapted to address the autonomous and decentralized management of energy in distributed systems.…”