663 research outputs found
Multi-level diversity promotion strategies for Grammar-guided Genetic Programming
Grammar-guided Genetic Programming (G3P) is a family of Evolutionary Algorithms that can evolve programs in any language described by a context-free grammar. The most widespread members of this family are based on an indirect representation: a sequence of bits or integers (the genotype) is transformed into a string of the language (the phenotype) by means of a mapping function, and eventually into a fitness value. Unfortunately, the flexibility brought by this mapping is also likely to introduce non-locality phenomena, reduce diversity, and hamper the effectiveness of the algorithm. In this paper, we experimentally characterize how population diversity, measured at different levels, varies for four popular G3P approaches. We then propose two strategies for promoting diversity which are general, independent both from the specific problem being tackled and from the other components of the Evolutionary Algorithm, such as genotype-phenotype mapping, selection criteria, and genetic operators. We experimentally demonstrate their efficacy in a wide range of conditions and from different points of view. The results also confirm the preponderant importance of the phenotype-level analyses in diversity promotion
Evolvability signatures of generative encodings: beyond standard performance benchmarks
Evolutionary robotics is a promising approach to autonomously synthesize
machines with abilities that resemble those of animals, but the field suffers
from a lack of strong foundations. In particular, evolutionary systems are
currently assessed solely by the fitness score their evolved artifacts can
achieve for a specific task, whereas such fitness-based comparisons provide
limited insights about how the same system would evaluate on different tasks,
and its adaptive capabilities to respond to changes in fitness (e.g., from
damages to the machine, or in new situations). To counter these limitations, we
introduce the concept of "evolvability signatures", which picture the
post-mutation statistical distribution of both behavior diversity (how
different are the robot behaviors after a mutation?) and fitness values (how
different is the fitness after a mutation?). We tested the relevance of this
concept by evolving controllers for hexapod robot locomotion using five
different genotype-to-phenotype mappings (direct encoding, generative encoding
of open-loop and closed-loop central pattern generators, generative encoding of
neural networks, and single-unit pattern generators (SUPG)). We observed a
predictive relationship between the evolvability signature of each encoding and
the number of generations required by hexapods to adapt from incurred damages.
Our study also reveals that, across the five investigated encodings, the SUPG
scheme achieved the best evolvability signature, and was always foremost in
recovering an effective gait following robot damages. Overall, our evolvability
signatures neatly complement existing task-performance benchmarks, and pave the
way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary
figures. Accepted at Information Sciences journal (in press). Supplemental
videos are available online at, see http://goo.gl/uyY1R
Investigations into Lamarckism, Baldwinism and Local Search in Grammatical Evolution Guided by Reinforcement
Grammatical Evolution Guided by Reinforcement is an extension of Grammatical Evolution that tries to improve the evolutionary process adding break a learning process for all the individuals in the population. With this aim, each individual is given a chance to learn through a reinforcement learning mechanism during its lifetime. The learning process is completed with a Lamarckian mechanism in which an original genotype is replaced by the best learnt genotype for the individual. In a way, Grammatical Evolution Guided by Reinforcement shares an important feature with other hybrid algorithms, i.e. global search in the evolutionary process combined with local search in the learning process. In this paper the role of the Lamarck Hypothesis is reviewed and a solution inspired only in the Baldwin effect is included as well. Besides, different techniques about the trade-off between exploitation and exploration in the reinforcement learning step followed by Grammatical Evolution Guided by Reinforcement are studied. In order to evaluate the results, the system is applied on two different domains: a simple autonomous navigation problem in a simulated Kephera robot and a typical Boolean function problem
Baldwinian accounts of language evolution
Since Hinton & Nowlan published their seminal paper (Hinton & Nowlan 1987), the
neglected evolutionary process of the Baldwin effect has been widely acknowledged.
Especially in the field of language evolution, the Baldwin effect (Baldwin 1896d,
Simpson 1953) has been expected to salvage the long-lasting deadlocked situation of
modern linguistics: i.e., it may shed light on the relationship between environment
and innateness in the formation of language.However, as intense research of this evolutionary theory goes on, certain robust
difficulties have become apparent. One example is genotype-phenotype correlation.
By computer simulations, both Yamauchi (1999, 2001) and Mayley (19966) show
that for the Baldwin effect to work legitimately, correlation between genotypes and
phenotypes is the most essential underpinning. This is due to the fact that this type
of the Baldwin effect adopts as its core mechanism Waddington's (1975) "genetic
assimilation". In this mechanism, phenocopies have to be genetically closer to the
innately predisposed genotype. Unfortunately this is an overly naiive assumption
for the theory of language evolution. As a highly complex cognitive ability, the
possibility that this type of genotype-phenotype correlation exists in the domain of
linguistic ability is vanishingly small.In this thesis, we develop a new type of mechanism, called "Baldwinian Niche
Construction (BNC), that has a rich explanatory power and can potentially over¬
come this bewildering problem of the Baldwin effect. BNC is based on the theory
of niche construction that has been developed by Odling-Smee et al. (2003). The
incorporation of the theory into the Baldwin effect was first suggested by Deacon
(1997) and briefly introduced by Godfrey-Smith (2003). However, its formulation
is yet incomplete.In the thesis, first, we review the studies of the Baldwin effect in both biology
and the study of language evolution. Then the theory of BNC is more rigorously
developed. Linguistic communication has an intrinsic property that is fundamentally described in the theory of niche construction. This naturally leads us to the
theoretical necessity of BNC in language evolution. By creating a new linguistic
niche, learning discloses a previously hidden genetic variance on which the Baldwin
'canalizing' effect can take place. It requires no genetic modification in a given
genepool. There is even no need that genes responsible for learning occupy the
same loci as genes for the innate linguistic knowledge. These and other aspects of
BNC are presented with some results from computer simulations
Genetic evolution of sorting programs through a novel genotype-phenotype mapping
This paper presents an adaptable genetic evolutionary system, which includes an innovative approach to
mapping genotypes to phenotypes through XML rules. The evolutionary system was originally created to
evolve Regular Expressions (REs) to automate the extraction of web information. However, the system has
been adapted to work with a completely different domain – Complete Software Programs – to demonstrate
the flexibility of this approach. Specifically, the paper concentrates on the evolution of 'Sorting' programs .
Experiments show that our evolutionary system is successful and can be adapted to work for challenging
domains with minimum effort
Automated retrieval and extraction of training course information from unstructured web pages
Web Information Extraction (WIE) is the discipline dealing with the discovery, processing and extraction of specific pieces of information from semi-structured or unstructured web pages. The World Wide Web comprises billions of web pages and there is much need for systems that will locate, extract and integrate the acquired knowledge into organisations practices. There are some commercial, automated web extraction software packages, however their success comes from heavily involving their users in the process of finding the relevant web pages, preparing the system to recognise items of interest on these pages and manually dealing with the evaluation and storage of the extracted results.
This research has explored WIE, specifically with regard to the automation of the extraction and validation of online training information. The work also includes research and development in the area of automated Web Information Retrieval (WIR), more specifically in Web Searching (or Crawling) and Web Classification. Different technologies were considered, however after much consideration, Naïve Bayes Networks were chosen as the most suitable for the development of the classification system. The extraction part of the system used Genetic Programming (GP) for the generation of web extraction solutions. Specifically, GP was used to evolve Regular Expressions, which were then used to extract specific training course information from the web such as: course names, prices, dates and locations.
The experimental results indicate that all three aspects of this research perform very well, with the Web Crawler outperforming existing crawling systems, the Web Classifier performing with an accuracy of over 95% and a precision of over 98%, and the Web Extractor achieving an accuracy of over 94% for the extraction of course titles and an accuracy of just under 67% for the extraction of other course attributes such as dates, prices and locations. Furthermore, the overall work is of great significance to the sponsoring company, as it simplifies and improves the existing time-consuming, labour-intensive and error-prone manual techniques, as will be discussed in this thesis. The prototype developed in this research works in the background and requires very little, often no, human assistance
Evolving an ecology of mathematical expressions with grammatical evolution
This is the author’s version of a work that was accepted for publication in Biosystems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Biosystems, 111, 2, (2013) DOI: 10.1016/j.biosystems.2012.12.004This paper describes the use of grammatical evolution to obtain an ecology of artificial beings
associated with mathematical functions, whose fitness is also defined mathematically. The system
allows “parasite” species and “parasites of parasites” to develop, and supports the simultaneous
evolution of several ecological niches. The use of standard measurements makes it possible to
explore the influence of the number of niches or the presence of parasites on “biological” diversity
and similar functions. Our results suggest that some of the features of biological evolution depend
more on the genetic substrate and natural selection than on the actual phenotypic expression of that
substrate
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