Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering val...

Finding an optimal path for a robot in a soccer field involves different parameters such as the positions of the robot, positions of the obstacles, etc. Due to simplicity and smoothness of Ferguson Spline, it has been employed for path planning between arbitrary points on the field in many research teams. In order to optimize the parameters of Ferguson Spline some evolutionary or intelligent al...

This report is based on the work I have done for my Master Thesis project. The project as a whole consists of research done in the eld of evolutionary computation, and it is split into two distinct parts. The main theme is adaptive evolutionary algorithms. The rst part covers the research done on solving binary constraint satisfaction problems using adaptive evolutionary algorithms. This involv...

The optimal dimensional synthesis for planar mechanisms using differential evolution (DE) is demonstrated. Four examples are included: in the first case, the synthesis of a mechanism for hybrid-tasks, considering path generation, function generation, and motion generation, is carried out. The second and third cases pertain to path generation, with and without prescribed timing. Finally, the syn...

Differential Evolution algorithm is a simple yet reliable and robust evolutionary algorithm for numeric optimization. However, fine-tuning control parameters of DE algorithm is a tedious and time-consuming task thus became a major challenge for its application. This paper introduces a novel self-adaptive method for tuning the amplification parameters F of DE dynamically. This method sampled app...

The Differential Evolution algorithm goes back to the class of Evolutionary Algorithms and inherits its philosophy and concept. Possessing only three control parameters (size of population, differentiation and recombination constants) Differential Evolution has promising characteristics of robustness and convergence. In this paper we introduce a new principle of Energetic Selection. It consists...

The differential evolution (DE) algorithm with a new differential mutation base strategy, namely best of random, is applied to the synthesis of unequally spaced antenna arrays. In the best of random mutation strategy, the best individual among three randomly chosen individuals is used as the mutation base while the other two are for the vector difference. Hence a good balance of diversity and e...