A Framework for Knowledge Integrated Evolutionary Algorithms

Authors:
Ahmed Hallawa, Anil Yaman, Giovanni Iacca, and Gerd Ascheid
Book Title:
Applications of Evolutionary Computation: 20th European Conference, EvoApplications 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, Part I
Volume:
10199
Series:
Lecture Notes in Computer Science (LNCS)
Publisher:
Springer International Publishing
Address:
Cham
Pages:
653–669
Date:
2017
ISBN:
978-3-31955-849-3
DOI:
10.1007/978-3-319-55849-3_42
Language:
English

BibTeX

@inproceedings{hallawa17,
author = {Ahmed Hallawa, Anil Yaman, Giovanni Iacca, and Gerd Ascheid},
booktitle = {Applications of Evolutionary Computation: 20th European Conference, EvoApplications 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, Part I},
title = {A Framework for Knowledge Integrated Evolutionary Algorithms},
year = {2017},
address = {Cham},
volume = {10199},
pages = {653-669},
ISBN= {978-3-31955-849-3},
publisher = {Springer International Publishing},
series = { Lecture Notes in Computer Science (LNCS)},
doi = {10.1007/978-3-319-55849-3_42},
}

Abstract

One of the main reasons for the success of Evolutionary Algorithms (EAs) is their general-purposeness, i.e. the fact that they can be applied in a straight forward manner to a broad range of optimization problems, without any specific prior knowledge. On the other hand, it has been shown that incorporating a priori knowledge, such as expert knowledge or empirical findings, can significantly improve the performance of an EA. However, integrating knowledge in EAs poses numerous challenges. It is often the case that the features of the search space are unknown, hence any knowledge associated with the search space properties can be hardly used. In addition, a priori knowledge is typically problem-specific and hard to generalize. In this paper, we propose a framework, called Knowledge Integrated Evolutionary Algorithm (KIEA), which facilitates the integration of existing knowledge into EAs. Notably, the KIEA framework is EA-agnostic, i.e. it works with any evolutionary algorithm, problem-independent, i.e. it is not dedicated to a specific type of problems and expandable, i.e. its knowledge base can grow over time. Furthermore, the framework integrates knowledge while the EA is running, thus optimizing the consumption of computational power. In the preliminary experiments shown here, we observe that the KIEA framework produces in the worst case an 80% improvement on the converge time, w.r.t. the corresponding “knowledge-free” EA counterpart.

Download

hallawa17.pdf 1007 K

News >> News >> News

ICE invites applications for a Postdoc position

Area: Application Specific Computing Systems and Hardware Architectures

EC selected TETRAMAX for funding, the successor of TETRACOM

We are glad to announce that the EC will grant the H2020 Innovation Action TETRAMAX with

User login

Login

Forgot your password?