A Sequential Learnable Evolutionary Algorithm with a Novel Knowledge Base Generation Method

Sequential learnable evolutionary algorithm (SLEA) provides an algorithm selection framework for solving the black box continuous design optimization problems. An algorithm pool consists of set of established algorithms. A knowledge base is trained offline. SLEA uses the algorithm-problem features to select the best algorithm from the algorithm pool. Given a problem, the default algorithm is run for the initial round. After that, an algorithm-problem feature is collected and used to map to the most similar problem in the knowledge base. Then the best algorithm for solving the problem is used in the second round. This process iterates until \( n \) rounds have been made. It is revealed that the algorithm-problem feature is a good problem identifier, thus SLEA performs well on the known problems that have been encountered. However, the performance on those unknown problems is limited if the knowledge base is biased. In this paper, we propose a modified SLEA, which performs the training process using a novel method. A relatively unbiased knowledge base is formed. Experimental results show that the modified SLEA maintains the performance of SLEA on solving the CEC 2013 test suite, while it performs better than SLEA on solving a set of randomly generated max-set of Gaussian test problems.

[1]  Marcus Gallagher,et al.  A general-purpose tunable landscape generator , 2006, IEEE Transactions on Evolutionary Computation.

[2]  Jano I. van Hemert,et al.  Evolving Combinatorial Problem Instances That Are Difficult to Solve , 2006, Evolutionary Computation.

[3]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[4]  K. F. Fong,et al.  Simulation–optimization of solar–thermal refrigeration systems for office use in subtropical Hong Kong , 2011 .

[5]  Riccardo Poli,et al.  Evolving problems to learn about particle swarm and other optimisers , 2005, 2005 IEEE Congress on Evolutionary Computation.

[6]  Xin Zhang,et al.  A novel artificial bee colony algorithm for HVAC optimization problems , 2013 .

[7]  Ilya Loshchilov,et al.  CMA-ES with restarts for solving CEC 2013 benchmark problems , 2013, 2013 IEEE Congress on Evolutionary Computation.

[8]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[9]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[10]  Rajkumar Roy,et al.  Recent advances in engineering design optimisation: Challenges and future trends , 2008 .

[11]  K. F. Fong,et al.  Energy management and design of centralized air-conditioning systems through the non-revisiting strategy for heuristic optimization methods , 2010 .

[12]  Mario A. Muñoz,et al.  ICARUS: Identification of complementary algorithms by uncovered sets , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[13]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[14]  Mario A. Muñoz,et al.  Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges , 2015, Inf. Sci..

[15]  Heike Trautmann,et al.  Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models , 2016, GECCO.

[16]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[17]  Yang Lou,et al.  Sequential Learnable Evolutionary Algorithm: A Research Program , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[18]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[19]  S. L. Ho,et al.  An Improved Artificial Bee Colony Algorithm for Optimal Design of Electromagnetic Devices , 2013, IEEE Transactions on Magnetics.

[20]  Mauricio Zambrano-Bigiarini,et al.  Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements , 2013, 2013 IEEE Congress on Evolutionary Computation.

[21]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[22]  Lars Kotthoff,et al.  Algorithm Selection for Combinatorial Search Problems: A Survey , 2012, AI Mag..