Fitness landscapes and memetic algorithm design
New ideas in optimization
Trust-region methods
Swarm intelligence
Field Guide to Dynamical Recurrent Networks
Field Guide to Dynamical Recurrent Networks
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Life and its molecules: a brief introduction
AI Magazine
Comparing evolutionary algorithms on the problem of network inference
Proceedings of the 8th annual conference on Genetic and evolutionary computation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Benchmarking Derivative-Free Optimization Algorithms
SIAM Journal on Optimization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Inferring gene regulatory networks from expression profiles is a challenging problem that has been tackled using many different approaches. When posed as an optimization problem, the typical goal is to minimize the value of an error measure, such as the relative squared error, between the real profiles and those generated with a model whose parameters are to be optimized. In this paper, we use dynamic recurrent neural networks to model regulatory interactions and study systematically the "fitness landscape" that results from measuring the relative squared error. Although the results of the study indicate that the generated landscapes have a positive fitness-distance correlation, the error values span several orders of magnitude over very short distance variations. This suggests that the fitness landscape has extremely deep valleys, which can make general-purpose state-of-the-art continuous optimization algorithms exhibit a very poor performance. Further results, obtained from an analysis based on perturbations of the optimal network topology, support approaches in which the spaces of network topologies and of network parameters are decoupled.