Browsing by Author "Bazargani, Mosab"
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- Affine image registration using genetic algorithms and evolutionary strategiesPublication . Bazargani, Mosab; Lobo, Fernando Miguel Pais de GraçaThis thesis investigates the application of evolutionary algorithms to align two or more 2-D images by means of image registration. The proposed search strategy is a transformation parameters-based approach involving the affine transform. A noisy objective function is proposed and tested using two well-known evolutionary algorithms (EAs), the genetic algorithm (GA) as well as the evolutionary strategies (ES) that are suitable for this particular ill-posed problem. In contrast with GA, which was originally designed to work on binary representation, ES was originally developed to work in continuous search spaces. Surprisingly, results of the proposed real coded genetic algorithm are far superior when compared to results obtained from evolutionary strategies’ framework for the problem at hand. The real coded GA uses Simulated Binary Crossover (SBX), a parent-centric recombination operator that has shown to deliver a good performance in many optimization problems in the continuous domain. In addition, a new technique for matching points, between a warped and static images by using a randomized ordering when visiting the points during the matching procedure, is proposed. This new technique makes the evaluation of the objective function somewhat noisy, but GAs and other population-based search algorithms have been shown to cope well with noisy fitness evaluations. The results obtained from GA formulation are competitive to those obtained by the state-of-the-art classical methods in image registration, confirming the usefulness of the proposed noisy objective function and the suitability of SBX as a recombination operator for this type of problem.
- A cutoff time strategy based on the coupon collector’s problemPublication . Lobo, Fernando G.; Bazargani, Mosab; Burke, Edmund K.Throughout the course of an optimization run, the probability of yielding further improvement becomes smaller as the search proceeds, and eventually the search stagnates. Under such a state, letting the algorithm continue to run is a waste of time as there is little hope that subsequent improvement can be made. The ability to detect the stagnation point is therefore of prime importance. If such a point can be detected reliably, then it is possible to make better use of the computing resources, perhaps restarting the algorithm at the stagnation point, either with the same or with a different parameter configuration. This paper proposes a cutoff time strategy. It presents a method that is able to reliably detect the stagnation point for one-point stochastic local search algorithms applied to combinatorial optimization problems. The strategy is derived from the coupon collector’s problem, and is scalable based on the employed perturbation operator and its induced neighbourhood size, as well as from feedback from the search. The suitability and scalability of the method is illustrated with the Late Acceptance Hill-Climbing algorithm on a comprehensive set of benchmark instances of three well-known combinatorial optimization problems: the Travelling Salesman Problem, the Quadratic Assignment Problem, and the Permutation Flowshop Scheduling Problem.
- When hillclimbers beat genetic algorithms in multimodal optimizationPublication . Lobo, F.J.; Bazargani, MosabThis paper investigates the performance of multistart next ascent hillclimbing and well-known evolutionary algorithms incorporating diversity preservation techniques on instances of the multimodal problem generator. This generator induces a class of problems in the bitstringdomain which is interesting to study from a theoretical perspective in the context of multimodal optimization, as it is a generalization of the classical OneMax and TwoMax functions for an arbitrary number of peaks. An average-case runtime analysis for multistart next ascent hill-climbing is presented for uniformly distributed equal-height instances of this class of problems. It is shown empirically that conventional niching and mating restriction techniques incorporated in an evolutionary algorithm are not sufficient to make them competitive with the hillclimbing strategy. We conjecture the reason for this behaviour is the lack of structure in the space of local optima on instances of this problem class, which makes an optimization algorithm unable to exploit information from one optimum to infer where another optimum might be. When no such structure exist, it seems that the best strategy for discovering all optima is a brute-force one. Overall, our study gives insights with respect to the adequacy of hillclimbers and evolutionary algorithms for multimodal optimization, depending on properties of the fitness landscape.