Add to ORKGAbstract: This paper describes a new Evolutionary Programming algorithm based on Self-Organised Criticality. When tested on a range of problems drawn from real-world applications in science and engineering, it performed better than a variety of gradient descent, direct search and genetic algorithms. It proved capable of delivering high quality results faster, and is simple, robust and highly parallel
This paper presents a population-based evolutionary computation model for solving continuous constra...
Due to the character of the original source materials and the nature of batch digitization, quality ...
Most genetic programming systems use hard-coded genetic operators that are applied according to user...
Abstract. This paper describes a new Evolutionary Programming algorithm based on Self-Organised Crit...
This book compares the performance of various evolutionary computation (EC) techniques when they are...
The complexity of current mechanisms continues to increase and their users keep on demanding even mo...
Although object-oriented conceptual software design is difficult to learn and perform, computational...
The goal of this research is to investigate the application of evolutionary search to the process of...
Computational optimization methods are most often used to find a single or multiple optimal or near-...
This paper introduces the notion of using co-evolution to adapt the penalty factors of a fitness fun...
An increasing number of successful applications of Evolutionary Algorithms (EAs) within Computer Aid...
Evolutionary algorithms are successively applied to wide optimization problems in the engineering, m...
Genetic programming (GP) is an automated method for creating a working computer program from a high-...
Decision making features occur in all fields of human activities such as science and technological a...
This work develops a framework of design evolution to support and automate the generation and evalua...
This paper presents a population-based evolutionary computation model for solving continuous constra...
Due to the character of the original source materials and the nature of batch digitization, quality ...
Most genetic programming systems use hard-coded genetic operators that are applied according to user...
Abstract. This paper describes a new Evolutionary Programming algorithm based on Self-Organised Crit...
This book compares the performance of various evolutionary computation (EC) techniques when they are...
The complexity of current mechanisms continues to increase and their users keep on demanding even mo...
Although object-oriented conceptual software design is difficult to learn and perform, computational...
The goal of this research is to investigate the application of evolutionary search to the process of...
Computational optimization methods are most often used to find a single or multiple optimal or near-...
This paper introduces the notion of using co-evolution to adapt the penalty factors of a fitness fun...
An increasing number of successful applications of Evolutionary Algorithms (EAs) within Computer Aid...
Evolutionary algorithms are successively applied to wide optimization problems in the engineering, m...
Genetic programming (GP) is an automated method for creating a working computer program from a high-...
Decision making features occur in all fields of human activities such as science and technological a...
This work develops a framework of design evolution to support and automate the generation and evalua...
This paper presents a population-based evolutionary computation model for solving continuous constra...
Due to the character of the original source materials and the nature of batch digitization, quality ...
Most genetic programming systems use hard-coded genetic operators that are applied according to user...