Kent McClymont
ABSTRACT
This paper provides an updated survey of new literature in, and related to, the field of problem understanding which has been published or made available since January 2012. The bibliographic information from the survey is available online at http://bit.ly/ZWoY3X. The survey covers work on the topics of: Benchmark Problems; Problem Decomposition & Multiobjectivisation; Landscape Analysis; Problem Difficulty; and Algorithm Selection & Performance Prediction. In addition, special attention is drawn to three recently published and excellent topic specific surveys. A side note is also made regarding the parallels between problem understanding, and specifically landscape analysis and the work of fitness landscape analysis in theoretical, conventional and evolutionary biology.
- Length scale for characterising continuous optimization problems. In C. A. Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia, and M. Pavone, editors, Parallel Problem Solving from Nature - PPSN XII, volume 7491 of Lecture Notes in Computer Science, pages 407--416. Springer Berlin Heidelberg, 2012. Google Scholar
Digital Library
- T. Abell, Y. Malitsky, and K. Tierney. Fitness landscape based features for exploiting black-box optimization problem structure. Technical report, Technical Report TR-2012-162, IT University of Copenhagen, 2012.Google Scholar
- A. Almakhlafi and J. Knowles. Benchmarks for maintenance scheduling problems in power generation. In Evolutionary Computation (CEC), 2012 IEEE Congress on, pages 1--8, 2012.Google ScholarCross Ref
- D. Ashlock and S. McNicholas. Fitness landscapes of evolved apoptotic cellular automata. Evolutionary Computation, IEEE Transactions on, 17(2):198--212, 2013.Google ScholarDigital Library
- A. Auger, S. Finck, N. Hansen, R. Ros, et al. Bbob 2009: Comparison tables of all algorithms on all noiseless functions. Technical Report RT-388, INRIA, 2010.Google Scholar
- B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß. Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. In Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, GECCO '12, pages 313--320, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- R. Burkard, S. Karisch, and F. Rendl. Qaplib - a quadratic assignment problem library. Journal of Global Optimization, 10(4):391--403, 1997. Google ScholarDigital Library
- P. Caamano, F. Bellas, J. Becerra, V. Diaz, and R. Duro. Experimental analysis of the relevance of fitness landscape topographical characterization. In Evolutionary Computation (CEC), 2012 IEEE Congress on, pages 1--8, 2012.Google ScholarCross Ref
- F. Chicano and E. Alba. Exact computation of the fitness-distance correlation for pseudoboolean functions with one global optimum. In J.-K. Hao and M. Middendorf, editors, Evolutionary Computation in Combinatorial Optimization, volume 7245 of Lecture Notes in Computer Science, pages 111--123. Springer Berlin Heidelberg, 2012. Google ScholarDigital Library
- F. Chicano, F. Daolio, G. Ochoa, S. Vérel, M. Tomassini, and E. Alba. Local optima networks, landscape autocorrelation and heuristic search performance. In Parallel Problem Solving from Nature-PPSN XII, pages 337--347. Springer, 2012. Google ScholarDigital Library
- F. Chicano, G. Luque, and E. Alba. A methodology to find the elementary landscape decomposition of combinatorial optimization problems. Evolutionary Computation, 19(4):597--637, 2011. Google ScholarDigital Library
- F. Chicano, G. Luque, and E. Alba. Autocorrelation measures for the quadratic assignment problem. Applied Mathematics Letters, 25(4):698--705, 2012.Google ScholarCross Ref
- K. Crona, D. Greene, and M. Barlow. The peaks and geometry of fitness landscapes. Journal of Theoretical Biology, 317(0):1--10, 2013.Google ScholarCross Ref
- J. Franke and J. Krug. Evolutionary accessibility in tunably rugged fitness landscapes. Journal of Statistical Physics, 148(4):705--722, 2012.Google ScholarCross Ref
- E. Galván-López, J. Mcdermott, M. O'Neill, and A. Brabazon. Defining locality as a problem difficulty measure in genetic programming. Genetic Programming and Evolvable Machines, 12(4):365--401, 2011. Google ScholarDigital Library
- N. Hansen, A. Auger, S. Finck, and R. Ros. Real-parameter black-box optimization bench-marking 2009: Experimental setup. Technical Report RR-6828, INRIA, 2009.Google Scholar
- H. Ishibuchi, M. Yamane, and Y. Nojima. Difficulty in evolutionary multiobjective optimization of discrete objective functions with different granularities. In R. C. Purshouse, P. J. Fleming, C. M. Fonseca, S. Greco, and J. Shaw, editors, Evolutionary Multi-Criterion Optimization, volume 7811 of Lecture Notes in Computer Science, pages 230--245. Springer Berlin Heidelberg, 2013.Google ScholarCross Ref
- Y. Jin. A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing, 9(1):3--12, 2005. Google ScholarDigital Library
- J. Lehman and K. O. Stanley. Exploiting open-endedness to solve problems through the search for novelty. 2008.Google Scholar
- J. Lehman and K. O. Stanley. Abandoning objectives: Evolution through the search for novelty alone. Evolutionary Computation, 19(2):189--223, 2011. Google ScholarDigital Library
- H. Li and Q. Zhang. Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii. Evolutionary Computation, IEEE Transactions on, 13(2):284--302, 2009. Google ScholarDigital Library
- X. Li, K. Tang, M. N. Omidvar, Z. Yang, K. Qin, and H. China. Benchmark functions for the cec'2013 special session and competition on large-scale global optimization. Technical report, Evolutionary Computation and Machine Learning Group, RMIT University, 2013.Google Scholar
- D. Lochtefeld and F. Ciarallo. Multiobjectivization via helper-objectives with the tunable objectives problem. Evolutionary Computation, IEEE Transactions on, 16(3):373--390, 2012.Google ScholarDigital Library
- K. M. Malan and A. P. Engelbrecht. A survey of techniques for characterising fitness landscapes and some possible ways forward. Information Sciences, 2013. To Appear. Google ScholarDigital Library
- K. McClymont and E. Keedwell. 1st understanding problems workshop (gecco-up). In T. Soule, editor, GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, New York, NY, USA, 2012. ACM. 910122.Google Scholar
- K. McClymont, D. Walker, and M. Dupenois. The lay of the land: a brief survey of problem understanding. In Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, GECCO Companion '12, pages 425--432, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, and G. Rudolph. Exploratory landscape analysis. In Proceedings of the 13th annual conference on Genetic and evolutionary computation. GECCO '11, pages 829--836. ACM, New York, NY, USA, 2011. Google ScholarDigital Library
- C. Müller and I. F. Sbalzarini. Global characterization of the cec 2005 fitness landscapes using fitness-distance analysis. In Applications of Evolutionary Computation, volume 6624 of Lecture Notes in Computer Science, pages 294--303. Springer Berlin Heidelberg, 2011. Google ScholarDigital Library
- M. A. Muñoz, M. Kirley, and S. K. Halgamuge. A meta-learning prediction model of algorithm performance for continuous optimization problems. In Parallel Problem Solving from Nature-PPSN XII, pages 226--235. Springer, 2012. Google ScholarDigital Library
- A. L. Nelson, G. J. Barlow, and L. Doitsidis. Fitness functions in evolutionary robotics: A survey and analysis. Robot. Auton. Syst., 57(4):345--370, 2009. Google ScholarDigital Library
- E. Pitzer and M. Affenzeller. A comprehensive survey on fitness landscape analysis. In Recent Advances in Intelligent Engineering Systems, volume 378 of Studies in Computational Intelligence, pages 161--191. Springer Berlin Heidelberg, 2012.Google ScholarCross Ref
- E. Pitzer, M. Affenzeller, A. Beham, and S. Wagner. Comprehensive and automatic fitness landscape analysis using heuristiclab. In Computer Aided Systems Theory - EUROCAST 2011, volume 6927 of Lecture Notes in Computer Science, pages 424--431. Springer Berlin Heidelberg, 2012. Google ScholarDigital Library
- E. Pitzer, A. Beham, and M. Affenzeller. Automatic algorithm selection for the quadratic assignment problem using fitness landscape analysis. In Evolutionary Computation in Combinatorial Optimization, volume 7832 of Lecture Notes in Computer Science, pages 109--120. Springer Berlin Heidelberg, 2013. Google ScholarDigital Library
- E. Pitzer, S. Vonolfen, A. Beham, M. Affenzeller, V. Bolshakov, and G. Merkuryeva. Structural analysis of vehicle routing problems using general fitness landscape analysis and problem specific measures. In 14th International Asia Pacific Conference on Computer Aided System Theory, pages 36--38, 2012.Google Scholar
- P. Rohlfshagen and X. Yao. Dynamic combinatorial optimisation problems: an analysis of the subset sum problem. Soft Computing, 15(9):1723--1734, 2011. Google ScholarDigital Library
- P. Rohlfshagen and X. Yao. Dynamic combinatorial optimization problems: A fitness landscape analysis. In Metaheuristics for Dynamic Optimization, volume 433 of Studies in Computational Intelligence, pages 79--97. Springer Berlin Heidelberg, 2013.Google ScholarCross Ref
- D. Saxena, Q. Zhang, J. a. A. Duro, and A. Tiwari. Framework for many-objective test problems with both simple and complicated pareto-set shapes. In Evolutionary Multi-Criterion Optimization, volume 6576 of Lecture Notes in Computer Science, pages 197--211. Springer Berlin Heidelberg, 2011. Google ScholarDigital Library
- P. K. Shukla, M. A. Braun, and H. Schmeck. Theory and algorithms for finding knees. In Evolutionary Multi-Criterion Optimization, volume 7811 of Lecture Notes in Computer Science, pages 156--170. Springer Berlin Heidelberg, 2013.Google ScholarCross Ref
- K. Smith-Miles and L. Lopes. Measuring instance difficulty for combinatorial optimization problems. Computers & Operations Research, 39(5):875--889, 2012. Google ScholarDigital Library
- I. G. Szendro, M. F. Schenk, J. Franke, J. Krug, and J. A. G. M. de Visser. Quantitative analyses of empirical fitness landscapes. Journal of Statistical Mechanics: Theory and Experiment, 2013(01):P01005, 2013.Google ScholarCross Ref
Index Terms
- Recent advances in problem understanding: changes in the landscape a year on
Recommendations
Bag of local landscape features for fitness landscape analysis
Evolutionary computation is a research field dealing with black-box and complex optimization problems whose fitness landscapes are usually unknown in advance. It is difficult to select an appropriate evolutionary algorithm and parameters for a given ...
Problem understanding through landscape theory
GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computationIn order to understand the structure of a problem we need to measure some features of the problem. Some examples of measures suggested in the past are autocorrelation and fitness-distance correlation. Landscape theory, developed in the last years in the ...
An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design
Evolutionary Algorithms (EAs) have been widely employed to solve water resources problems for nearly two decades with much success. However, recent research in hyperheuristics has raised the possibility of developing optimisers that adapt to the ...
Comments