TY - GEN
T1 - A framework for knowledge integrated evolutionary algorithms
AU - Hallawa, A.
AU - Yaman, A.
AU - Iacca, G.
AU - Ascheid, G.
PY - 2017
Y1 - 2017
N2 - © Springer International Publishing AG 2017.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.
AB - © Springer International Publishing AG 2017.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.
U2 - 10.1007/978-3-319-55849-3_42
DO - 10.1007/978-3-319-55849-3_42
M3 - Conference contribution
SN - 9783319558486
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 653
EP - 659
BT - Applications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings
A2 - Hidalgo, J.I.
A2 - Cotta, C.
A2 - Hu, T.
A2 - Tonda, A.
A2 - Burrelli, P.
A2 - Coler, M.
A2 - Iacca, G.
A2 - Kampouridis, M.
A2 - Mora Garcia, A.M.
A2 - Squillero, G.
A2 - Brabazon, A.
A2 - Haasdijk, E.
A2 - Heinerman, J.
A2 - D Andreagiovanni, F.
A2 - Bacardit, J.
A2 - Nguyen, T.T.
A2 - Silva, S.
A2 - Tarantino, E.
A2 - Esparcia-Alcazar, A.I.
A2 - Ascheid, G.
A2 - Glette, K.
A2 - Cagnoni, S.
A2 - Kaufmann, P.
A2 - de Vega, F.F.
A2 - Mavrovouniotis, M.
A2 - Zhang, M.
A2 - Divina, F.
A2 - Sim, K.
A2 - Urquhart, N.
A2 - Schaefer, R.
PB - Springer Verlag
T2 - 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017
Y2 - 19 April 2017 through 21 April 2017
ER -