# Adaptive Penalties for Evolutionary Graph Coloring

@inproceedings{Eiben1997AdaptivePF, title={Adaptive Penalties for Evolutionary Graph Coloring}, author={Agoston E. Eiben and J. K. van der Hauw}, booktitle={Artificial Evolution}, year={1997} }

In this paper we consider a problem independent constraint handling mechanism, Stepwise Adaptation of Weights (SAW) and show its working on graph coloring problems. SAW-ing technically belongs to the penalty function based approaches and amounts to modifying the penalty function during the search. We show that it has a twofold benefit. First, it proves to be rather insensitive to its technical parameters, thereby providing a general, problem independent way to handle constrained problemsâ€¦Â

## 52 Citations

### A Study of Evaluation Functions for the Graph K-Coloring Problem

- Computer ScienceArtificial Evolution
- 2007

A new evaluation function for the well-known graph K-coloring problem is introduced that takes into account not only the number of conflicting vertices, but alsoherent information related to the structure of the graph.

### Comparing evolutionary algorithms on binary constraint satisfaction problems

- Computer ScienceIEEE Trans. Evol. Comput.
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This paper presents a concise overview and an extensive performance comparison of all these EAs for solving constraint satisfaction problems (CSP) on a systematically generated test suite of random binary CSPs using a theoretical model based on a random problem instance generator.

### Test problems and representations for graph evolution

- Computer Science2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)
- 2014

This study fills in a gap in the literature by presenting two scalable families of benchmark functions that are matching the eccentricity sequences of graphs, the second is locating graphs that are relatively easy to color non-optimally.

### Solving Temporal Constraint Satisfaction Problems with Heuristic Based Evolutionary Algorithms

- Computer Science2008 20th IEEE International Conference on Tools with Artificial Intelligence
- 2008

The results show that heuristics do not promise better performance for solving TCSPs and the basic genetic algorithm and microgenetic iterative descendant are the most effective ones.

### A New Crossover for Solving Constraint Satisfaction Problems

- Computer ScienceEvoCOP
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This paper proposes a novel crossover specifically designed for solving CSPs, which enables the solving of large and hard problem instances and is able to compete with the efficient MAC-based Abscon 109 solver for random problem instances.

### New Low Cost and Undedicated Genetic Operators title

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### A New Parallel GA-Based Method for Constraint Satisfaction Problems

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### SAWing on symmetry

- Computer ScienceProceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
- 1999

The behavior of mutation-based evolutionary algorithms on highly symmetric binary constraint satisfaction problems is investigated with empirical methods and it is observed that SAWing has little effect when the local optima of the symmetric problems are not very strong.

### Chapter 10 How to Handle Constraints with Evolutionary Algorithms

- Biology
- 2001

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### Evolutionary Computation and Constraint Satisfaction

- Computer ScienceHandbook of Computational Intelligence
- 2015

This chapter focuses on the combination of evolutionary computation techniques and constraint satisfaction problems (CSP s) and an important prelude to the work covered here as it advocates itself as an alternative approach to programming.

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