Results 1 - 10
of
74
Evolving a self-repairing, self-regulating French flag organism
, 2004
"... A method for evolving programs that construct multicellular struc-tures (organisms) is described. The paper concentrates on the difficult problem of evolving a cell program that constructs a fixed size French flag. We obtain and analyze an organism that shows a remarkable ability to repair itself ..."
Abstract
-
Cited by 87 (15 self)
- Add to MetaCart
(Show Context)
A method for evolving programs that construct multicellular struc-tures (organisms) is described. The paper concentrates on the difficult problem of evolving a cell program that constructs a fixed size French flag. We obtain and analyze an organism that shows a remarkable ability to repair itself when subjected to severe damage. Its behaviour resembles the regenerative power of some living organisms.
Evolving Developmental Programs for Adaptation, Morphogenesis and Self-Repair
- Proceeding of ECAL 2003
, 2003
"... A method for evolving a developmental program inside a cell to create multicellular organisms of arbitrary size and characteristics is described. ..."
Abstract
-
Cited by 65 (23 self)
- Add to MetaCart
(Show Context)
A method for evolving a developmental program inside a cell to create multicellular organisms of arbitrary size and characteristics is described.
Evolving the program for a cell: from french flags to boolean circuits
, 2003
"... The development of an entire organism from a single cell is one of the most profound and awe inspiring phenomena in the whole of the natural world. The complexity of living systems itself dwarfs anything that man has produced. This is all the more the case for the processes that lead to ..."
Abstract
-
Cited by 46 (7 self)
- Add to MetaCart
(Show Context)
The development of an entire organism from a single cell is one of the most profound and awe inspiring phenomena in the whole of the natural world. The complexity of living systems itself dwarfs anything that man has produced. This is all the more the case for the processes that lead to
Redundant Representation in Evolutionary Computation.
- Evolutionary Computation,
, 2003
"... Abstract This paper discusses how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and non-synonymously redund ..."
Abstract
-
Cited by 30 (4 self)
- Add to MetaCart
(Show Context)
Abstract This paper discusses how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and non-synonymously redundant representations. Representations are synonymously redundant if the genotypes that represent the same phenotype are very similar to each other. Non-synonymously redundant representations do not allow genetic operators to work properly and result in a lower performance of evolutionary search. When using synonymously redundant representations, the performance of selectorecombinative genetic algorithms (GAs) depends on the modification of the initial supply. We have developed theoretical models for synonymously redundant representations that show the necessary population size to solve a problem and the number of generations goes with O(2 kr /r), where kr is the order of redundancy and r is the number of genotypic building blocks (BB) that represent the optimal phenotypic BB. As a result, uniformly redundant representations do not change the behavior of GAs. Only by increasing r, which means overrepresenting the optimal solution, does GA performance increase. Therefore, non-uniformly redundant representations can only be used advantageously if a-priori information exists regarding the optimal solution. The validity of the proposed theoretical concepts is illustrated for the binary trivial voting mapping and the real-valued link-biased encoding. Our empirical investigations show that the developed population sizing and time to convergence models allow an accurate prediction of the empirical results.
A Developmental method for growing Graphs and Circuits
- 5th Int. Conf. on Evolvable Systems
, 2003
"... A review is given of approaches to growing neural networks and electronic circuits. A new method for growing graphs and circuits using a developmental process is discussed. The method is inspired by the view that the cell is the basic unit of biology. Programs that construct circuits are evolved ..."
Abstract
-
Cited by 29 (7 self)
- Add to MetaCart
(Show Context)
A review is given of approaches to growing neural networks and electronic circuits. A new method for growing graphs and circuits using a developmental process is discussed. The method is inspired by the view that the cell is the basic unit of biology. Programs that construct circuits are evolved to build a sequence of digital circuits at user specified iterations. The programs can be run for an arbitrary number of iterations so circuits of huge size could be created that could not be evolved. It is shown that the circuit building programs are capable of correctly predicting the next circuit in a sequence of larger even parity functions. The new method however finds building specific circuits more difficult than a non-developmental method.
Self-Modifying Cartesian Genetic Programming
, 2007
"... In nature, systems with enormous numbers of components (i.e. cells) are evolved from a relatively small genotype. It has not yet been demonstrated that artificial evolution is sufficient to make such a system evolvable. Consequently researchers have been investigating forms of computational developm ..."
Abstract
-
Cited by 27 (14 self)
- Add to MetaCart
(Show Context)
In nature, systems with enormous numbers of components (i.e. cells) are evolved from a relatively small genotype. It has not yet been demonstrated that artificial evolution is sufficient to make such a system evolvable. Consequently researchers have been investigating forms of computational development that may allow more evolvable systems. The approaches taken have largely used re-writing, multi- cellularity, or genetic regulation. In many cases it has been difficult to produce general purpose computation from such systems. In this paper we introduce computational development using a form of Cartesian Genetic Programming that includes self-modification operations. One advantage of this approach is that ab initio the system can be used to solve computational problems. We present results on a number of problems and demonstrate the characteristics and advantages that self-modification brings.
How Neutral Networks Influence Evolvability
, 2001
"... Evolutionary algorithms apply the process of variation, reproduction and selection to look for an individual capable of solving the task at hand. In order to improve the evolvability of a population we propose to copy important characteristics of nature's search space. Desired characteristic ..."
Abstract
-
Cited by 25 (0 self)
- Add to MetaCart
Evolutionary algorithms apply the process of variation, reproduction and selection to look for an individual capable of solving the task at hand. In order to improve the evolvability of a population we propose to copy important characteristics of nature's search space. Desired characteristics for a genotype-phenotype mapping are described and several highly redundant genotype-phenotype mappings are analyzed in the context of a population based search. We show that evolvability, de ned as the ability of random variations to sometimes produce improvement, is inuenced by the existence of neutral networks in genotype space. Redundant mappings allow the population to spread along the network of neutral mutations and the population is quickly able to recover after a change has occurred. The extent of the neutral networks aects the interconnectivity of the search space and thereby aects evolvability.
Evolution and Acquisition of Modules in Cartesian Genetic Programming
- In Proc. of the 7th European Conference on Genetic Programming, volume 3003 of LNCS
, 2004
"... Abstract. The paper presents for the first time automatic module acquisition and evolution within the graph based Cartesian Genetic Programming method. The method has been tested on a set of even parity problems and compared with Cartesian Genetic Programming without modules. Results are given that ..."
Abstract
-
Cited by 25 (14 self)
- Add to MetaCart
(Show Context)
Abstract. The paper presents for the first time automatic module acquisition and evolution within the graph based Cartesian Genetic Programming method. The method has been tested on a set of even parity problems and compared with Cartesian Genetic Programming without modules. Results are given that show that the new modular method evolves solutions up to 20 times quicker than the original non-modular method and that the speedup is more pronounced on larger problems. Analysis of some of the evolved modules shows that often they are lower order parity functions. Prospects for further improvement of the method are discussed. 1
Finding Needles in Haystacks Is Not Hard with Neutrality
- Proceedings of the 5th European Conference on Genetic Programming. Springer LNCS 2278
, 2002
"... Abstract. We propose building neutral networks in needle-in-haystack fitness landscapes to assist an evolutionary algorithm to perform search. The experimental results on four different problems show that this approach improves the search success rates in most cases. In situations where neutral netw ..."
Abstract
-
Cited by 23 (5 self)
- Add to MetaCart
(Show Context)
Abstract. We propose building neutral networks in needle-in-haystack fitness landscapes to assist an evolutionary algorithm to perform search. The experimental results on four different problems show that this approach improves the search success rates in most cases. In situations where neutral networks do not give performance improvement, no impairment occurs either. We also tested a hypothesis proposed in our previous work. The results support the hypothesis: when the ratio of adaptive/neutral mutations during neutral walk is close to the ratio of adaptive/neutral mutations at the fitness improvement step, the evolutionary search has a high success rate. Moreover, the ratio magnitudes indicate that more neutral mutations (than adaptive mutations) are required for the algorithms to find a solution in this type of search space. 1
Incorporating Characteristics of Human Creativity into an Evolutionary Art Algorithm
"... Figure 1. Source Darwin image with examples of evolved abstract portraits created using an automatic creative system. A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generatio ..."
Abstract
-
Cited by 21 (7 self)
- Add to MetaCart
(Show Context)
Figure 1. Source Darwin image with examples of evolved abstract portraits created using an automatic creative system. A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2 nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.
