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Adaptive-mutation compact genetic algorithm for dynamic environments

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Abstract

In recent years, the interest in studying nature-inspired optimization algorithms for dynamic optimization problems (DOPs) has been increasing constantly due to its importance in real-world applications. Several techniques such as hyperselection, change prediction, hypermutation and many more have been developed to address DOPs. Among these techniques, the hypermutation scheme has proved beneficial for addressing DOPs, but requires that the mutation factors be picked a priori and this is one of the limitations of the hypermutation scheme. This paper investigates variants of the recently proposed adaptive-mutation compact genetic algorithm (amcGA). The amcGA is made up of a change detection scheme and mutation schemes, where the degree of change regulates the probability of mutation (i.e. the probability of mutation is directly proportional to the degree of change). This paper also presents a change trend scheme for the amcGA so as to boost its performance whenever a change occurs. Experimental results show that the change trend and mutation schemes have an impact on the performance of the amcGA in dynamic environment and also indicate that the effect of the schemes depends on the dynamics of the environment as well as the dynamic problem being considered.

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References

  • Ahn CW, Ramakrishna RS (2003) Elitism-based compact genetic algorithms. IEEE Trans Evol Comput 7(4):367–385

    Article  Google Scholar 

  • Bektas T (2006) The multiple traveling salesman problem: an overview of formulations and solution procedures. Omega 34(3):209–219

    Article  Google Scholar 

  • Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: In congress on evolutionary computation CEC99, Citeseer

  • Branke J, Kaußler T, Smidt C, Schmeck H (2000) A multi-population approach to dynamic optimization problems. In: Parmee IC (ed) Evolutionary design and manufacture. Springer, pp 299–307

  • Branke J, Orbayı M, Uyar Ş (2006) The role of representations in dynamic knapsack problems. In: Rothlauf F, Branke J, Cagnoni S, Costa E, Cotta C, Drechsler R, Lutton E, Machado P, Moore JH, Romero J, Smith GD, Squillero G, Takagi H (eds) Applications of evolutionary computing. Springer, pp 764–775

  • Bui LT, Michalewicz Z, Parkinson E, Abello M (2012) Adaptation in dynamic environments: a case study in mission planning. IEEE Trans Evol Comput 16(2):190–209

    Article  Google Scholar 

  • Cobb H, Grefenstette J (1993) Genetic algorithms for tracking changing environments, Morgan Kaufmann Publishers Inc, San Francisco, CA, pp 523–530

  • Cobb HG (1990) An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report AIC-90-001, Naval Research Laboratory, Washington

  • Dasgupta D, McGregor DR (1993) SGA: a structured genetic algorithm. University of Strathclyde, Department of Computer Science

  • Dasgupta D, McGregor DR (1992) Nonstationary function optimization using the structured genetic algorithm. In: PPSN, pp 145–154

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Eiben A, Smit S (2012) Evolutionary algorithm parameters and methods to tune them. In: Youssef H, Eric M, Frédéric S (eds) Autonomous search. Springer, pp 15–36

  • Eiben A, Schut M, de Wilde A (2006) Boosting genetic algorithms with self-adaptive selection. In: IEEE congress on evolutionary computation. CEC 2006. pp 477–482

  • Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2):124–141

    Article  Google Scholar 

  • Fernandes CM, Guervós JJM, Rosa AC (2009) Using dissortative mating genetic algorithms to track the extrema of dynamic deceptive functions. CoRR abs/0904.3063

  • Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley, Boston

    MATH  Google Scholar 

  • Goldberg DE (2002) The design of innovation: lessons from and for competent genetic algorithms. Kluwer Academic Publishers, Norwell

    Book  MATH  Google Scholar 

  • Goldberg DE, Smith RE (1987) Nonstationary function optimization using genetic algorithms with dominance and diploidy. In: ICGA, pp 59–68

  • Gongora M, Passow B, Hopgood A (2009) Robustness analysis of evolutionary controller tuning using real systems. In: IEEE Congress on evolutionary computation. CEC ’09, pp 606–613

  • Grefenstette JJ et al (1992) Genetic algorithms for changing environments. PPSN, vol 2. San Francisco, CA, pp 137–144

  • Hansen N, Kern S (2004) Evaluating the cma evolution strategy on multimodal test functions. In: Yao X, Burke EK, Lozano JA, Smith J, Merelo-Guervos JJ, Bullinaria JA, Rowe JE, Tivno P, Kaban A, Schwefel H-P (eds) Parallel problem solving from nature-PPSN VIII. Springer, pp 282–291

  • Hansen N, Ostermeier A (1996) Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In: Proceedings of IEEE international conference on evolutionary computation, pp 312–317. doi:10.1109/ICEC.1996.542381

  • Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195

    Article  Google Scholar 

  • Harik G, Lobo F, Goldberg D (1999) The compact genetic algorithm. IEEE Trans Evol Comput 3(4):287–297

    Article  Google Scholar 

  • Harik G, Lobo F, Sastry K (2006) Linkage learning via probabilistic modeling in the extended compact genetic algorithm (ecga). In: Pelikan M, Sastry K, CantPaz E (eds) Scalable optimization via probabilistic modeling, studies in computational intelligence, vol 33. Springer, Berlin, pp 39–61

    Chapter  Google Scholar 

  • Hatzakis I, Wallace D (2006) Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proceedings of the 8th annual conference on genetic and evolutionary computation. ACM, New York, GECCO ’06, pp 1201–1208

  • Higuchi T, Iwata M, Keymeulen D, Sakanashi H, Murakawa M, Kajitani I, Takahashi E, Toda K, Salami N, Kajihara N, Otsu N (1999) Real-world applications of analog and digital evolvable hardware. IEEE Trans Evol Comput 3(3):220–235

    Article  Google Scholar 

  • Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments-a survey. IEEE Trans Evol Comput 9(3):303–317

    Article  Google Scholar 

  • Juang CF (2004) A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybern B Cybern 34(2):997–1006

    Article  Google Scholar 

  • Larraanaga P, Lozano JA (2001) Estimation of distribution algorithms: a new tool for evolutionary computation. Kluwer Academic Publishers, Norwell

    Google Scholar 

  • Martins JP, Fonseca CM, Delbem AC (2014) On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem. Neurocomputing 146:17–29

    Article  Google Scholar 

  • Mininno E, Cupertino F, Naso D (2008) Real-valued compact genetic algorithms for embedded microcontroller optimization. IEEE Trans Evol Comput 12(2):203–219

    Article  Google Scholar 

  • Morrison RW, De Jong KA (2000) Triggered hypermutation revisited. In: Proceedings of the 2000 congress on evolutionary computation, IEEE, vol 2, pp 1025–1032

  • Nelson AL, Barlow GJ, Doitsidis L (2009) Fitness functions in evolutionary robotics: a survey and analysis. Robot Auton Syst 57(4):345–370

    Article  Google Scholar 

  • Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol Comput 6:1–24

    Article  Google Scholar 

  • Passow B, Gongora M, Coupland S, Hopgood A (2008) Real-time evolution of an embedded controller for an autonomous helicopter. In: IEEE congress on evolutionary computation. CEC 2008 (IEEE World Congress on Computational Intelligence), pp 2538–2545

  • Pedersen GK, Yang Z (2006) Multi-objective pid-controller tuning for a magnetic levitation system using nsga-ii. In: Proceedings of the 8th annual conference on genetic and evolutionary computation. ACM, New York, GECCO ’06, pp 1737–1744

  • Pelikan M, Goldberg D, Lobo F (2000) A survey of optimization by building and using probabilistic models. In: Proceedings of the 2000 American control conference, vol 5, pp 3289–3293

  • Rohlfshagen P, Yao X (2009) The dynamic knapsack problem revisited: a new benchmark problem for dynamic combinatorial optimisation. In: Giacobini M, Brabazon A, Cagnoni S, Di Caro G, Ekrt A, Esparcia-Alczar A, Farooq M, Fink A, Machado P (eds) Applications of evolutionary computing. Lecture notes in computer science, vol 5484. Springer, Berlin, pp 745–754

    Google Scholar 

  • Sastry K, Abbass HA, Goldberg D (2005) Sub-structural niching in non-stationary environments. In: Geoffrey IW, Xinghuo Y (eds) AI 2004: advances in artificial intelligence. Springer, pp 873–885

  • Shah R, Reed P (2011) Comparative analysis of multiobjective evolutionary algorithms for random and correlated instances of multiobjective d-dimensional knapsack problems. Eur J Oper Res 211(3):466–479

    Article  MathSciNet  Google Scholar 

  • Simões A, Costa E (2009a) Improving prediction in evolutionary algorithms for dynamic environments. In: Proceedings of the 11th annual conference on genetic and evolutionary computation. ACM, pp 875–882

  • Simões A, Costa E (2009b) Prediction in evolutionary algorithms for dynamic environments using markov chains and nonlinear regression. In: Proceedings of the 11th annual conference on genetic and evolutionary computation. ACM, pp 883–890

  • Uzor C, Gongora M, Coupland S, Passow B (2014a) Adaptive mutation in dynamic environments. In: 14th UK workshop on computational intelligence (UKCI), pp 1–7

  • Uzor CJ, Gongora M, Coupland S, Passow BN (2014b) Real-world dynamic optimization using an adaptive-mutation compact genetic algorithm. In: 2014 IEEE Symposium on computational intelligence in dynamic and uncertain environments (CIDUE), pp 17–23

  • Vavak Frank, Fogarty Terence C, Jukes Ken (1996) A genetic algorithm with variable range of local search for tracking changing environments. In: Voigt H-M, Ebeling W, Rechenberg I, Schwefel H-P (eds) Parallel problem solving from nature PPSN IV. Springer, pp 376–385

  • Wang H, Yang S, Ip W, Wang D (2009) Adaptive primal-dual genetic algorithms in dynamic environments. IEEE Trans Syst Man Cybern B Cybern 39(6):1348–1361

    Article  Google Scholar 

  • Whitley D (1991) Fundamental principles of deception. In: Foundations of genetic algorithms 1991 (FOGA 1), pp 221–241

  • Woldesenbet Y, Yen G (2009) Dynamic evolutionary algorithm with variable relocation. IEEE Trans Evol Comput 13(3):500–513

    Article  Google Scholar 

  • Yang S (2008) Genetic algorithms with memory-and elitism-based immigrants in dynamic environments. Evol Comput 16(3):385–416

    Article  Google Scholar 

  • Yang S, Richter H (2009) Hyper-learning for population-based incremental learning in dynamic environments. In: IEEE congress on evolutionary computation. CEC ’09, pp 682–689

  • Yang S, Tinós R (2007) A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int J Autom Comput 4(3):243–254

    Article  Google Scholar 

  • Yang S, Tinos R (2008) Hyper-selection in dynamic environments. In: IEEE congress on evolutionary computation. CEC 2008 (IEEE World Congress on Computational Intelligence), pp 3185–3192

  • Yang S, Yao X (2005) Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput 9(11):815–834

    Article  MATH  Google Scholar 

  • Yang S, Yao X (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Trans Evol Comput 12(5):542–561

    Article  Google Scholar 

  • Yang S, Jiang Y, Nguyen TT (2013) Metaheuristics for dynamic combinatorial optimization problems. IMA J Manag Math 24(4):451–480

    Article  MathSciNet  MATH  Google Scholar 

  • Yu E, Suganthan P (2009) Evolutionary programming with ensemble of explicit memories for dynamic optimization. In: IEEE congress on evolutionary computation. CEC ’09, pp 431–438

  • Yu X, Tang K, Yao X (2008) An immigrants scheme based on environmental information for genetic algorithms in changing environments. In: IEEE congress on evolutionary computation. CEC 2008 (IEEE World Congress on Computational Intelligence), pp 1141–1147

  • Zhu H, Jiao L, Pan J (2006) Multi-population genetic algorithm for feature selection. In: Jiao L, Wang L, Gao X, Liu J, Wu F (eds) Advances in natural computation. Lecture notes in computer science, vol 4222. Springer, Berlin, pp 480–487

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Centre for Computational Intelligence (CCI), De Montfort University, Leicester, UK

    Chigozirim J. Uzor, Mario Gongora & Simon Coupland

  2. Centre for Computational Intelligence (CCI), De Montfort University’s ITS Research Group (DIGITS), De Montfort University, Leicester, UK

    Benjamin N. Passow

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  1. Chigozirim J. Uzor
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  2. Mario Gongora
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  3. Simon Coupland
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  4. Benjamin N. Passow
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Correspondence to Chigozirim J. Uzor.

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Communicated by D. Neagu.

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Uzor, C.J., Gongora, M., Coupland, S. et al. Adaptive-mutation compact genetic algorithm for dynamic environments. Soft Comput 20, 3097–3115 (2016). https://doi.org/10.1007/s00500-016-2195-x

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