Evaluating landscape characteristics of dynamic benchmark functions

This work provides a landscape analysis of the dynamic benchmark functions commonly used in multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic benchmarks do not significantly affect landscape features; thus suggesting a lack of representation for problems whose landscape features vary over time.

[1]  Jing J. Liang,et al.  Novel composition test functions for numerical global optimization , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[2]  Jürgen Branke,et al.  Memory enhanced evolutionary algorithms for changing optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[3]  Marcus Gallagher,et al.  Length Scale for Characterising Continuous Optimization Problems , 2012, PPSN.

[4]  Katherine M. Malan Characterising continuous optimisation problems for particle swarm optimisation performance prediction , 2014 .

[5]  Vesselin K. Vassilev Fitness landscapes and search in the evolutionary design of digital circuits , 2000 .

[6]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

[7]  R. Salomon Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms. , 1996, Bio Systems.

[8]  Terry Jones,et al.  Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms , 1995, ICGA.

[9]  Changhe Li,et al.  Benchmark generator for the IEEE WCCI-2012 competition on evolutionary computation for dynamic optimization problems. Technical Report 2011. , 2011 .

[10]  L. Darrell Whitley,et al.  The dispersion metric and the CMA evolution strategy , 2006, GECCO.

[11]  Andries Petrus Engelbrecht,et al.  Quantifying ruggedness of continuous landscapes using entropy , 2009, 2009 IEEE Congress on Evolutionary Computation.

[12]  Vesselin K. Vassilev,et al.  Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application , 2003 .

[13]  Andries Petrus Engelbrecht,et al.  Steep gradients as a predictor of PSO failure , 2013, GECCO '13 Companion.

[14]  Andrew M. Sutton,et al.  PSO and multi-funnel landscapes: how cooperation might limit exploration , 2006, GECCO.

[15]  Changhe Li,et al.  A Generalized Approach to Construct Benchmark Problems for Dynamic Optimization , 2008, SEAL.

[16]  J. Urgen Branke Evolutionary Approaches to Dynamic Optimization Problems -a Survey , 1999 .

[17]  Julian Francis Miller,et al.  Information Characteristics and the Structure of Landscapes , 2000, Evolutionary Computation.

[18]  Andries Petrus Engelbrecht,et al.  A progressive random walk algorithm for sampling continuous fitness landscapes , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[19]  Bernd Bischl,et al.  Exploratory landscape analysis , 2011, GECCO '11.