Now showing items 11-17 of 17
Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search
Multi-Objective Evolutionary Algorithms (MOEAs) and transport simulators have been widely utilized to optimise traffic signal timings with multiple objectives. However, traffic simulations require much processing time and ...
The Importance of Being Structured: a Comparative Study on Multi Stage Memetic Approaches
Memetic Computing (MC) is a discipline which studies optimization algorithms and sees them as structures of operators, the memes. Although the choice of memes is crucial for an effective algorithmic design, special attention ...
A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms
(Springer Berlin Heidelberg, 2014-11)
The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the ...
A comparison of three Differential Evolution strategies in terms of early convergence with different population sizes
Differential Evolution (DE) is a popular population-based continuous optimization algorithm that generates new candidate solutions by perturbing the existing ones, using scaled differences of randomly selected solutions ...
Structural bias in population-based algorithms
Abstract Challenging optimisation problems are abundant in all areas of science and industry. Since the 1950s, scientists have responded to this by developing ever-diversifying families of ‘black box’ optimisation algorithms. ...
Multicriteria adaptive differential evolution for global numerical optimization
(IOS Press, 2015-02-01)
Differential evolution (DE) has become a prevalent tool for global optimization problems since it was proposed in 1995. As usual, when applying DE to a specific problem, determining the most proper strategy and its associated ...