— In this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolv...
Yang Yan, Hongfeng Wang, Dingwei Wang, Shengxiang ...
—Much of the computational complexity in employing evolutionary algorithms as optimization tool is due to the fitness function evaluation that may either not exist or be computat...
Mohsen Davarynejad, Mohammad R. Akbarzadeh-Totonch...
— Hierarchical menus are widely used as a standard user interface in modern applications that use GUIs. The performance of the menu depends on many factors: structure, layout, co...
— Differential Evolution (DE) is well known as a simple and efficient algorithm for global optimization over continuous spaces. This article provides a simple mathematical model ...
Abstract— The Learnable Evolution Model (LEM) was introduced by Michalski in 2000, and involves interleaved bouts of evolution and learning. Here we investigate LEM in (we think)...
— In this article we present results from experiments where a edge detector was learned from scratch by EANT2, a method for evolutionary reinforcement learning. The detector is c...
— This paper presents Natural Evolution Strategies (NES), a novel algorithm for performing real-valued ‘black box’ function optimization: optimizing an unknown objective func...
Abstract— This paper explores the idea of neutrality in heuristic optimization algorithms. In particular, the effect of having multiple levels of neutrality in representations is...
— Graphics processor units are fast, inexpensive parallel computing devices. Recently there has been great interest in harnessing this power for various types of scientific comp...
— In recent years, several approaches have been developed for genetic algorithms to enhance their performance in dynamic environments. Among these approaches, one kind of methods...