Abstract. An evolutionary algorithm is combined with an application-specific developmental scheme in order to evolve efficient arbitrarily large sorting networks. First, a small sorting network (that we call the embryo) has to be prepared to solve the trivial instance of a problem. Then the evolved program (the constructor) is applied on the embryo to create a larger sorting network (solving a larger instance of the problem). Then the same constructor is used to create a new instance of the sorting network from the created larger sorting network and so on. The proposed approach allowed us to rediscover the conventional principle of insertion which is traditionally used for constructing large sorting networks. Furthermore, the principle was ...
This paper explores the use of growth processes, or embryogenies, to map genotypes to phenotypes wit...
The research presented in this thesis is concerned with optimising the structure of Artificial Neura...
Standard methods for inducing both the structure and weight values of recurrent neural networks fit ...
A method is presented for the construction of arbitrary even-input sorting networks exhibiting bette...
This paper demonstrates how non-typed genetic programming may be used to evolve sorting networks; sp...
This paper deals with sorting networks design using Cartesian Genetic Programming and coevolution. S...
This work focuses on application of rewriting systems in the context of biology-inspired development...
This work provides an introduction to the evolutionary algorithms and evolutionary design. It also d...
Genetic Programming is applied to the task of evolving general iterative sorting algorithms. A conne...
Sorting networks are an interesting class of parallel sorting algorithms with applications in multi-...
Abstract. A fundamental issue in evolutionary learning is the definition of the solution representat...
Abstract. Sorting Networks (SN) are efficient tools to sort an input data sequence. They are compose...
Evolutionary algorithms that use embryogenesis in the creation of individuals have several desirable...
is applied to the task of evolving general recursive sorting algorithms. We studied the effects of l...
Abstract—Biological genomes have evolved over a period of millions of years and comprise thousands o...
This paper explores the use of growth processes, or embryogenies, to map genotypes to phenotypes wit...
The research presented in this thesis is concerned with optimising the structure of Artificial Neura...
Standard methods for inducing both the structure and weight values of recurrent neural networks fit ...
A method is presented for the construction of arbitrary even-input sorting networks exhibiting bette...
This paper demonstrates how non-typed genetic programming may be used to evolve sorting networks; sp...
This paper deals with sorting networks design using Cartesian Genetic Programming and coevolution. S...
This work focuses on application of rewriting systems in the context of biology-inspired development...
This work provides an introduction to the evolutionary algorithms and evolutionary design. It also d...
Genetic Programming is applied to the task of evolving general iterative sorting algorithms. A conne...
Sorting networks are an interesting class of parallel sorting algorithms with applications in multi-...
Abstract. A fundamental issue in evolutionary learning is the definition of the solution representat...
Abstract. Sorting Networks (SN) are efficient tools to sort an input data sequence. They are compose...
Evolutionary algorithms that use embryogenesis in the creation of individuals have several desirable...
is applied to the task of evolving general recursive sorting algorithms. We studied the effects of l...
Abstract—Biological genomes have evolved over a period of millions of years and comprise thousands o...
This paper explores the use of growth processes, or embryogenies, to map genotypes to phenotypes wit...
The research presented in this thesis is concerned with optimising the structure of Artificial Neura...
Standard methods for inducing both the structure and weight values of recurrent neural networks fit ...