Sequential Learnable Evolutionary Algorithm: A Research Program

Evolutionary algorithms are typically run several times in design optimization problems and the best solution taken. We propose a novel online algorithm selection framework that learns to use the best algorithm based on previous runs, hence in effect using different and better algorithms as the sear...

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Vydané v:	2015 IEEE International Conference on Systems, Man, and Cybernetics s. 2841 - 2848
Hlavní autori:	Shiu Yin Yuen, Xin Zhang, Yang Lou
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.10.2015
Predmet:	Algorithm design and analysis algorithm selection Classification algorithms design optimization problems Evolutionary computation Machine learning algorithms multi-restart algorithm new research program Optimization Portfolios Prediction algorithms
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Shrnutí:	Evolutionary algorithms are typically run several times in design optimization problems and the best solution taken. We propose a novel online algorithm selection framework that learns to use the best algorithm based on previous runs, hence in effect using different and better algorithms as the search progresses. First, a set of algorithms are run on a benchmark problem suite. Given a new problem, a default algorithm is run and its convergence characteristics are recorded. This is used to map to the problem database to find the most similar problem. In turn, the database returns the best algorithm for this problem and this algorithm is run in the second iteration and so on, aiming to home onto the most suitable algorithm for the problem. The resulting algorithm, named Sequential Learnable Evolutionary algorithm (SLEA), outperforms Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with multi-restarts. SLEA is also applied to a new problem, a real world application, and learns its characteristics. Experimental results show that it can correctly select the best algorithm for the problem. Finally, this paper proposes a new research program which learns the algorithm-problem mapping through solving real world problems accessed through the web and worldwide cooperation through Wikipedia.
DOI:	10.1109/SMC.2015.495