Evolutionary Algorithms for Dynamic Environments: Prediction Using Linear Regression and Markov Chains

In this work we investigate the use of prediction mechanisms in Evolutionary Algorithms for dynamic environments. These mechanisms, linear regression and Markov chains, are used to estimate the generation when a change in the environment will occur, and also to predict to which state (or states) the environment may change, respectively. Different types of environmental changes were studied. A memory-based evolutionary algorithm empowered by these two techniques was successfully applied to several instances of the dynamic bit matching problem.

[1]  Jürgen Branke,et al.  A Multi-population Approach to Dynamic Optimization Problems , 2000 .

[2]  Hajime Kita,et al.  Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm , 1996, PPSN.

[3]  Katja Verbeeck,et al.  A “Futurist” approach to dynamic environments , 2000 .

[4]  D. Vere-Jones.  Markov Chains , 1972, Nature.

[5]  David W. Pearson,et al.  An Immune System-Based Genetic Algorithm to Deal with Dynamic Environments: Diversity and Memory , 2003, ICANNGA.

[6]  Peter A. N. Bosman,et al.  Inventory management and the impact of anticipation in evolutionary stochastic online dynamic optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[7]  Anabela Simões,et al.  Improving memory’s usage in evolutionary algorithms for changing environments , 2007, 2007 IEEE Congress on Evolutionary Computation.

[8]  L. Miles,et al.  2000 , 2000, RDH.

[9]  Xin Yao,et al.  Experimental study on population-based incremental learning algorithms for dynamic optimization problems , 2005, Soft Comput..

[10]  Peter A. N. Bosman.  Learning and Anticipation in Online Dynamic Optimization , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[11]  Shengxiang Yang,et al.  Evolutionary Computation in Dynamic and Uncertain Environments , 2007, Studies in Computational Intelligence.

[12]  Mark Wineberg,et al.  Enhancing the GA's Ability to Cope with Dynamic Environments , 2000, GECCO.

[13]  Xin Yao,et al.  Parallel Problem Solving from Nature PPSN VI , 2000, Lecture Notes in Computer Science.

[14]  John J. Grefenstette,et al.  Genetic Algorithms for Changing Environments , 1992, PPSN.

[15]  Peter A. N. Bosman,et al.  Learning, anticipation and time-deception in evolutionary online dynamic optimization , 2005, GECCO '05.

[16]  Peter A. N. Bosman,et al.  Computationally Intelligent Online Dynamic Vehicle Routing by Explicit Load Prediction in an Evolutionary Algorithm , 2006, PPSN.

[17]  Xiaodong Li,et al.  Using regression to improve local convergence , 2007, 2007 IEEE Congress on Evolutionary Computation.

[18]  Anabela Simões,et al.  Variable-Size Memory Evolutionary Algorithm to Deal with Dynamic Environments , 2009, EvoWorkshops.

[19]  Hajime Kita,et al.  Adaptation to a Changing Environment by Means of the Thermodynamical Genetic Algorithm , 1999 .

[20]  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).

[21]  Anabela Simões,et al.  Using Linear Regression to Predict Changes in Evolutionary Algorithms dealing with Dynamic Environments , 2007 .

[22]  Jürgen Branke,et al.  Anticipation in Dynamic Optimization: The Scheduling Case , 2000, PPSN.

[23]  Shengxiang Yang,et al.  Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments , 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

[24]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[25]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

[26]  H. J. Arnold.  Introduction to the Practice of Statistics , 1990 .

[27]  Peter Secretan.  Learning , 1965, Mental Health.

[28]  Helen G. Cobb,et al.  An Investigation into the Use of Hypermutation as an Adaptive Operator in Genetic Algorithms Having Continuous, Time-Dependent Nonstationary Environments , 1990 .

[29]  Phillip D. Stroud,et al.  Kalman-extended genetic algorithm for search in nonstationary environments with noisy fitness evaluations , 2001, IEEE Trans. Evol. Comput..