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The species conservation technique is a relatively new approach to finding multiple solutions of a multimodal optimization problem. When adopting such a technique, a species is defined as a group of individuals in a population that have similar characteristics and are dominated by the best individual, called the species seed. Species conservation techniques are used to identify species within a population...
MOEA/D is a generic multiobjective evolutionary optimization algorithm. MOEA/D needs a approach to decompose a multiobjective optimization problem into a number of single objective optimization problems. The commonly-used weighted sum approach and the Tchebycheff approach may not be able to handle disparately scaled objectives. This paper suggests a new decomposition approach, called NBI-style Tchebycheff...
Choosing and tuning an optimization procedure for a given class of nonlinear optimization problems is not an easy task. One way to proceed is to consider this as a tournament, where each procedure will compete in different `disciplines'. Here, disciplines could either be different functions, which we want to optimize, or specific performance measures of the optimization procedure. We would then be...
Cancer treatment by chemotherapy involves multiple applications of toxic drugs over a period of time. Optimising the schedule of these treatments can improve the outcome for the patient. A schedule of treatment and its effect on the tumour can be simulated by a mathematical growth model. However, when used in conjunction with a black-box optimisation algorithm such as an Evolutionary Algorithm (EA)...
This work focuses on the development of a parallel framework method to improve the effectiveness and the efficiency of the obtained solutions by Multi-objective Evolutionary Algorithms. Specifically, a parallel architecture based on JavaSpaces technology and an island paradigm model is proposed and tested on two important and complex computational problems: The Protein Structure Prediction and the...
Robustness is a key concern when developing a successful commercial evolutionary tool. In this paper we investigate the performance of Cultural Algorithms over the complete range of system complexities, from fixed to chaotic. In order to apply the Cultural Algorithm over all complexity classes we generalize on its co-evolutionary nature to keep the variation in the population across all complexities...
The concept of information gain has been adopted as tool to study the effectiveness of population-based optimizers; using this approach, it is possible to infer convergence properties for population-based optimizers. The experimental results have shown characteristic phase transition between exploration and exploitation phase during the evolutionary process and, moreover, the evidence that gain maximization...
The TORCS Endurance World Championship is an international competition in which programmers develop and tune their drivers to race against each other using TORCS, a state-of-the-art car racing simulator. In this work, we applied evolutionary computation to develop a driver for the 2009 edition of this competition. In particular, we focused on the optimization of the car setup of an existing driver...
Many-objective problems are difficult to solve using conventional multi-objective evolutionary algorithms (MOEAs) as these algorithms rely primarily on Pareto ranking to guide the search. This enforces only little selection pressure in a many-objective setting, since the population tends to become fully nondominated. A more feasible approach is to discover a low number of solutions within a region...
State-of-the art constrained multiobjective optimisation methods are often explored and demonstrated with the help of function optimisation problems from these accounts. It is sometimes hard for practitioners to extract good approaches for practical problems. In this paper we apply an evolutionary algorithm to a factual problem with realistic constraints and compare the effects of different operators...
Designing semiconductor devices is a complex problem, which requires accurate models and effective optimizers. We have introduced a new methodology based on derivative-free optimization evolutionary algorithms, which aim to achieve a near-optimal design. We have tested these algorithms on various semiconductor devices using a drift-diffusion model; the experimental results show that CMA-ES and DE...
We present basic ideas related to application of Dominance-based Rough Set Approach (DRSA) in interactive Evolutionary Multiobjective Optimization (EMO). In the proposed methodology, the preference information elicited by the decision maker in successive iterations consists in sorting some solutions in the current population into “relatively good” and “others”, or in comparing some pairs of solutions...
The purpose of this paper is to investigate the use of meta-heuristics as low-level heuristics in a hyper-heuristic framework. A novel multi-method hyper-heuristic algorithm which makes use of a number of common meta-heuristics is presented. Algorithm performance is evaluated on a diverse set of real parameter benchmark problems and meaningful conclusions are drawn with respect to the selection of...
A Coevolutionary, Hyper Heuristic approach to the optimization of Three-dimensional Process Plant Layouts (3DPPLs) is explored. By taking advantage of the natural problem decomposition, one population of layout heuristics, and another population of scheduling heuristics are coevolved. Generalized heuristics are evolved by training on multiple small problem instances, so that training time is reduced...
This paper investigates the issue of PID-controller parameters tuning for a greenhouse climate control system using Evolutionary Algorithms based on multiple performance measures such as good set-point tracking and smooth control signals. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is validated...
This paper introduces and benchmarks Sferesv2, a C++ framework designed to help researchers in evolutionary computation to make their code run as fast as possible on a multi-core computer. It is based on three main concepts: (1) including multi-core optimizations from the start of the design process; (2) providing state-of-the art implementations of well-selected current evolutionary algorithms (EA),...
The harmony search (HS) algorithm is one of the recent evolutionary computation techniques to solve optimization problems. To make it applicable for lot-streaming flow shop problems, a discrete variant of the HS algorithm (DHS) with job permutations representation is proposed. In the proposed DHS algorithm, a new improvisation scheme is designed to generate feasible job sequences. A local search algorithm...
Controller design is performed to meet some performance criteria. Some of them are the gain and phase margins, control effort, settling time, overshoot, amongst other indexes. Obtaining a controller or compensator that fits all these criteria (or some of them) for a closed loop control is done in both empirical and analytical procedures. The most of these procedures assumes the plant to be controlled...
The goal of multimodal optimisation is to identify multiple desirable optima of a fitness landscape within a single run of an evolutionary algorithm. Typically, one must resort to niching methods to perform this task, and such methods often require the use of a niche radius to distinguish between optima. Typically, this niche radius is difficult to set, leading to suboptimal performance of niching...
The planning of resources within a supply chain can prove to be a deciding factor in the success or failure of an operation. This research continues the authors' previous work using an extended Interval Type-2 Fuzzy Logic supply chain model, with an Evolutionary Algorithm to search for good resource plans. A set of enhanced experiments is conducted to validate our novel approach with optimal configurations,...
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