13,038 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
Neutrality: A Necessity for Self-Adaptation
Self-adaptation is used in all main paradigms of evolutionary computation to
increase efficiency. We claim that the basis of self-adaptation is the use of
neutrality. In the absence of external control neutrality allows a variation of
the search distribution without the risk of fitness loss.Comment: 6 pages, 3 figures, LaTe
Self-adaptive exploration in evolutionary search
We address a primary question of computational as well as biological research
on evolution: How can an exploration strategy adapt in such a way as to exploit
the information gained about the problem at hand? We first introduce an
integrated formalism of evolutionary search which provides a unified view on
different specific approaches. On this basis we discuss the implications of
indirect modeling (via a ``genotype-phenotype mapping'') on the exploration
strategy. Notions such as modularity, pleiotropy and functional phenotypic
complex are discussed as implications. Then, rigorously reflecting the notion
of self-adaptability, we introduce a new definition that captures
self-adaptability of exploration: different genotypes that map to the same
phenotype may represent (also topologically) different exploration strategies;
self-adaptability requires a variation of exploration strategies along such a
``neutral space''. By this definition, the concept of neutrality becomes a
central concern of this paper. Finally, we present examples of these concepts:
For a specific grammar-type encoding, we observe a large variability of
exploration strategies for a fixed phenotype, and a self-adaptive drift towards
short representations with highly structured exploration strategy that matches
the ``problem's structure''.Comment: 24 pages, 5 figure
Cell division and migration in a 'genotype' for neural networks
Much research has been dedicated recently to applying genetic algorithms to populations of
neural networks. However, while in real organisms the inherited genotype maps in complex
ways into the resulting phenotype, in most of this research the development process that
creates the individual phenotype is ignored. In this paper we present a model of neural
development which includes cell division and cell migration in addition to axonal growth and
branching. This reflects, in a very simplified way, what happens in the ontogeny of real
organisms. The development process of our artificial organisms shows successive phases of
functional differentiation and specialization. In addition, we find that mutations that affect
different phases of development have very different evolutionary consequences. A single
change in the early stages of cell division/migration can have huge effects on the phenotype
while changes in later stages have usually a less drammatic impact. Sometimes changes that
affect the first developental stages may be retained producing sudden changes in evolutionary
history
New insights on neutral binary representations for evolutionary optimization
This paper studies a family of redundant binary representations NNg(l, k), which are based on the mathematical formulation of error control codes, in particular, on linear block codes, which are used to add redundancy and neutrality to the representations. The analysis of the properties of uniformity, connectivity, synonymity, locality and topology of the NNg(l, k) representations is presented, as well as the way an (1+1)-ES can be modeled using Markov chains and applied to NK fitness landscapes with adjacent neighborhood.The results show that it is possible to design synonymously redundant representations that allow an increase of the connectivity between phenotypes. For easy problems, synonymously NNg(l, k) representations, with high locality, and where it is not necessary to present high values of connectivity are the most suitable for an efficient evolutionary search. On the contrary, for difficult problems, NNg(l, k) representations with low locality, which present connectivity between intermediate to high and with intermediate values of synonymity are the best ones. These results allow to conclude that NNg(l, k) representations with better performance in NK fitness landscapes with adjacent neighborhood do not exhibit extreme values of any of the properties commonly considered in the literature of evolutionary computation. This conclusion is contrary to what one would expect when taking into account the literature recommendations. This may help understand the current difficulty to formulate redundant representations, which are proven to be successful in evolutionary computation. (C) 2016 Elsevier B.V. All rights reserved
Myths and Legends of the Baldwin Effect
This position paper argues that the Baldwin effect is widely
misunderstood by the evolutionary computation community. The
misunderstandings appear to fall into two general categories.
Firstly, it is commonly believed that the Baldwin effect is
concerned with the synergy that results when there is an evolving
population of learning individuals. This is only half of the story.
The full story is more complicated and more interesting. The Baldwin
effect is concerned with the costs and benefits of lifetime
learning by individuals in an evolving population. Several
researchers have focussed exclusively on the benefits, but there
is much to be gained from attention to the costs. This paper explains
the two sides of the story and enumerates ten of the costs and
benefits of lifetime learning by individuals in an evolving population.
Secondly, there is a cluster of misunderstandings about the relationship
between the Baldwin effect and Lamarckian inheritance of acquired
characteristics. The Baldwin effect is not Lamarckian. A Lamarckian
algorithm is not better for most evolutionary computing problems than
a Baldwinian algorithm. Finally, Lamarckian inheritance is not a
better model of memetic (cultural) evolution than the Baldwin effect
Formation of modules in a computational model of embryogeny
An investigation is conducted into the effects of a complex mapping between genotype and phenotype upon a simulated evolutionary process. A model of embryogeny is utilised to grow simple French flag like patterns. The system is shown to display a phenotypic robustness to damage and it is argued that this is a result of a modularity forming within the mapping process which causes a functional grouping of sections of the genotype
Learning the dominance in diploid genetic algorithms for changing optimization problems
Using diploid representation with dominance
scheme is one of the approaches developed for genetic algorithms to address dynamic optimization problems. This paper proposes an adaptive dominance mechanism for diploid genetic algorithms in dynamic environments. In this scheme, the genotype to phenotype mapping in each gene locus is controlled by a dominance probability, which is learnt adaptively during the searching progress. The proposed dominance scheme isexperimentally compared to two other schemes for diploid genetic algorithms. Experimental results validate the efficiency of the dominance learning scheme
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