A Sequential Learnable Evolutionary Algorithm with a Novel Knowledge Base Generation Method

In this chapter an evolutionary algorithm is developed to learn a fuzzy knowledge base for the control of a soccer micro-robot from any configuration belonging to a grid of initial configurations, to hit the ball along the ball to goal line of sight. A relative coordinate system is used. Forward and reverse mode of the robot and its physical dimensions are incorporated, as well as special considerations to cases when in its initial configuration, the robot is touching the ball.

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Knowledge-Base Constrained Optimization Evolutionary Algorithm and its Applications

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.476 ◽

2014 ◽

Vol 536-537 ◽

pp. 476-480 ◽

Cited By ~ 1

Author(s):

Wen Long

Keyword(s):

Evolutionary Algorithms ◽

Constrained Optimization ◽

Evolutionary Algorithm ◽

Knowledge Base ◽

Optimization Problem ◽

Optimization Problems ◽

Engineering Optimization ◽

Constrained Optimization Problems ◽

Single Constraint ◽

Handling Technique

The most existing constrained optimization evolutionary algorithms (COEAs) for solving constrained optimization problems (COPs) only focus on combining a single EA with a single constraint-handling technique (CHT). As a result, the search ability of these algorithms could be limited. Motivated by these observations, we propose an ensemble method which combines different style of EA and CHT from the EA knowledge-base and the CHT knowledge-base, respectively. The proposed method uses two EAs and two CHTs. It randomly combines them to generate novel offspring individuals during each generation. Simulations and comparisons based on four benchmark COPs and engineering optimization problem demonstrate the effectiveness of the proposed approach.

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Outline of a new evolutionary algorithm for fuzzy systems learning

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/0954406021525340 ◽

2002 ◽

Vol 216 (5) ◽

pp. 557-570 ◽

Cited By ~ 5

Author(s):

D T Pham ◽

M Castellani

Keyword(s):

Evolutionary Algorithm ◽

Knowledge Base ◽

Automatic Generation ◽

Genetic Operators ◽

Fuzzy Logic Systems ◽

Adaptive Selection ◽

Genetic Search ◽

Acceptable Quality ◽

Systems Learning ◽

Level Of Knowledge

This paper describes a new evolutionary algorithm for the automatic generation of the knowledge base for fuzzy logic systems. In common with other evolutionary approaches, the approach adopted is to treat the problem of knowledge base generation as that of searching for a solution of an acceptable quality by applying genetic operators to a population of potential solutions. The algorithm presented dynamically adjusts the focus of the genetic search by dividing the population into three subgroups, each concerned with a different level of knowledge base optimization. The algorithm also includes a new adaptive selection routine that aims to keep the selection pressure constant throughout the learning phase.

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