Yesterday, in Lisbon, I defended my PhD thesis on Evolutionary Algorithms for dynamic optimization. The pdf of the document is here, and this is the abstract:
Many industrial applications have dynamic components that lead to variations of the fitness function and Genetic Algorithms (GAs) adaptiveness is an appropriate tool to solve this type of problems. The thesis proposes two new evolutionary methods to tackle dynamic problems. The first acts upon mating and avoids crossover between similar individuals, via a self-regulated mechanism, thus preserving genetic diversity. The second is a new mutation operator able to evolve self-regulated mutation rates with a particular distribution that is suited for dynamic optimization. Finally, an efficient hybrid method that combines both strategies is proposed. The objective and main claim is the possibility of designing nature-inspired protocols for GAs that are efficient when evolving on dynamic environments while preserving algorithms’ complexity and not requiring a priori information about the problem.
The proposals are tested on a wide range of problems and are able to outperform frequently other GAs, namely when the frequency of change is lower. The hybrid scheme proves to be particularly effective since it broadened the range of dynamics in which each method by itself excels. As projected, the proposed techniques are robust and do not increase parameters’ set, thus fulfilling necessary conditions for real-world applications.
As announced, Darrell Whitley gave a talk on the new operator his team has designed for the classic TSP.
After doing a review of all the classical techniques and operators used to solve this problem, he introduces his new crossover operator that tries not to introduce new edges in the offspring, acting thus as a pure recombination operator, instead of a crossover+mutation. This new operator is called perfect crossover, and is first respectful, and then it transmits alleles. It’s not always possible to apply it, but in samples studied it works 95% of the time. When used in combination with other mutation operators in an evolutionary algorithm, it yields very good results; not completely competitive with the best results known, but almost there.
It’s been great to have such a great talker in our weekly seminars. You can also check out his paper Tunneling between optima: partition crossover for the traveling salesman problem online
In this paper we analyse the resilience of a Peer-to-Peer (P2P) Evolutionary Algorithm (EA) subject to the following dynamics: computing nodes acting as peers leave the system independently from each other causing a collective effect known as churn. Since the P2P EA has been designed to tackle large instances of computationally expensive problems, we will assess its behaviour under these conditions, by performing a scalability analysis in five different scenarios using the Massively Multimodal Deceptive Problem as a benchmark.In all cases, the P2P EA reaches the success criterion without a penalty on the runtime. We show that the key to the algorithm resilience is to ensure enough peers at the beginning of the experiment; even if some of them leave, those that remain contain enough information to guarantee a reliable convergence.