Runtime analysis of the (1+1) evolutionary algorithm on strings over finite alphabets

In this work, we investigate a (1+1) Evolutionary Algorithm for optimizing functions over the space {0,...,<i>r</i>} <sup><i>n</i></sup>, where <i>r</i> is a positive integer. We show that for linear functions over {0,1,2}<sup><i>n</i></sup>, the expected runtime time of this algorithm is O(<i>n</i> log <i>n</i>). This result generalizes an existing result on pseudo-Boolean functions and is derived using drift analysis. We also show that for large values of <i>r</i>, no upper bound for the runtime of the (1+1) Evolutionary Algorithm for linear function on {0,...,<i>r</i>}<sup><i>n</i></sup> can be obtained with this approach nor with any other approach based on drift analysis with weight-independent linear potential functions.

[1]  Xin Yao,et al.  A study of drift analysis for estimating computation time of evolutionary algorithms , 2004, Natural Computing.

[2]  Christian Gunia,et al.  On the analysis of the approximation capability of simple evolutionary algorithms for scheduling problems , 2005, GECCO '05.

[3]  Anne Auger,et al.  Theory of Randomized Search Heuristics: Foundations and Recent Developments , 2011, Theory of Randomized Search Heuristics.

[4]  Benjamin Doerr,et al.  Multiplicative drift analysis , 2010, GECCO.

[5]  B. Hajek Hitting-time and occupation-time bounds implied by drift analysis with applications , 1982, Advances in Applied Probability.

[6]  Xin Yao,et al.  Drift analysis and average time complexity of evolutionary algorithms , 2001, Artif. Intell..

[7]  Frank Neumann,et al.  Bioinspired computation in combinatorial optimization: algorithms and their computational complexity , 2012, GECCO '12.

[8]  Ingo Wegener,et al.  The analysis of evolutionary algorithms on sorting and shortest paths problems , 2004, J. Math. Model. Algorithms.

[9]  Xin Yao,et al.  Erratum to: Drift analysis and average time complexity of evolutionary algorithms [Artificial Intelligence 127 (2001) 57-85] , 2002, Artif. Intell..

[10]  Ingo Wegener,et al.  Randomized local search, evolutionary algorithms, and the minimum spanning tree problem , 2004, Theor. Comput. Sci..

[11]  Martin Skutella,et al.  On the size of weights in randomized search heuristics , 2009, FOGA '09.

[12]  Benjamin Doerr,et al.  Edge-based representation beats vertex-based representation in shortest path problems , 2010, GECCO '10.

[13]  Jens Jägersküpper,et al.  A Blend of Markov-Chain and Drift Analysis , 2008, PPSN.

[14]  Thomas Jansen,et al.  On the analysis of the (1+1) evolutionary algorithm , 2002, Theor. Comput. Sci..

[15]  Benjamin Doerr,et al.  A tight analysis of the (1 + 1)-EA for the single source shortest path problem , 2007, IEEE Congress on Evolutionary Computation.

[16]  Benjamin Doerr,et al.  Drift analysis and linear functions revisited , 2010, IEEE Congress on Evolutionary Computation.

[17]  Martin Skutella,et al.  Evolutionary Algorithms and Matroid Optimization Problems , 2007, GECCO '07.

[18]  Frank Neumann,et al.  Computing single source shortest paths using single-objective fitness , 2009, FOGA '09.

[19]  Carsten Witt,et al.  Bioinspired Computation in Combinatorial Optimization , 2010, Bioinspired Computation in Combinatorial Optimization.