Automatic graph drawing and Stochastic Hill Climbing

In the literature of Evolutionary Computation, it is very strange to find papers where the results of Evolutionary Algorithms are compared to other algorithms. Stochastic Hill Climbing is a simple optimization algorithm that has shown a competitive performance with respect to many powerful algorithms in the solution of different problems. It has also outperformed some Evolutionary Algorithms in previous papers. Here we fairly review some of these papers. We also compare many Evolutionary Algorithms in the context of Graph Drawing. Graph Drawing addresses the problem of finding a representation of a graph that satisfies a given aesthetic objective. This problem has many practical applications in many fields such as Software Engineering, and VLSI Design. Our results demonstrate that Stochastic Hill Climbing is also the best algorithm in this context. We give some general guidelines in order to explain our results. Our explanations are based on landscape characteristics.

[1]  Hideo Tanaka,et al.  Genetic algorithms and neighborhood search algorithms for fuzzy flowshop scheduling problems , 1994 .

[2]  Ari Juels,et al.  Stochastic Hillclimbing as a Baseline Method for , 1994 .

[3]  Tim Jones.  Evolutionary Algorithms, Fitness Landscapes and Search , 1995 .

[4]  Joe Marks,et al.  A Parallel Genetic Algorithm for Network-Diagram Layout , 1991, ICGA.

[5]  S. Baluja.  An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics , 1995 .

[6]  L. Darrell Whitley,et al.  Test driving three 1995 genetic algorithms: New test functions and geometric matching , 1995, J. Heuristics.

[7]  L. Darrell Whitley,et al.  Search, Binary Representations and Counting Optima , 1999 .

[8]  Deniz Yuret.  From Genetic Algorithms to Efficient Optimization , 1994 .

[9]  J. Kingdon,et al.  Morphic search strategies , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[10]  Heinz Mühlenbein,et al.  The Equation for Response to Selection and Its Use for Prediction , 1997, Evolutionary Computation.

[11]  Erkki Mäkinen,et al.  TimGA - A Genetic Algorithm for Drawing Undirected Graphs , 1996 .

[12]  D. Wolpert,et al.  No Free Lunch Theorems for Search , 1995 .

[13]  A. Rosetea,et al.  Genetic Graph Drawing , 1970 .

[14]  Andrea G. B. Tettamanzi,et al.  Drawing Graphs with Evolutionary Algorithms , 1998 .

[15]  Ioannis G. Tollis,et al.  Algorithms for Drawing Graphs: an Annotated Bibliography , 1988, Comput. Geom..

[16]  Martin Wattenberg,et al.  Stochastic Hillclimbing as a Baseline Mathod for Evaluating Genetic Algorithms , 1995, NIPS.

[17]  Lawrence Davis,et al.  Bit-Climbing, Representational Bias, and Test Suite Design , 1991, ICGA.

[18]  M. Sebag,et al.  A society of hill-climbers , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).