18,571 research outputs found
Genetic representation and evolvability of modular neural controllers
The manual design of con- trol systems for robotic devices can be challenging. Methods for the automatic synthesis of control systems, such as the evolution of artificial neural networks, are thus widely used in the robotics community. However, in many robotic tasks where multiple interdependent control problems have to be solved simultaneously, the performance of conventional neuroevolution techniques declines. In this paper, we identify interference between the adaptation of different parts of the control system as one of the key challenges in the evolutionary synthesis of artificial neural networks.As modular net- work architectures have been shown to reduce the effects of such interference, we propose a novel, implicit modular genetic representation that allows the evolutionary algorithm to automatically shape modular network topologies. Our experiments with plastic neural networks in a simple maze learning task indicate that adding a modular genetic representation to a state-of-the-art implicit neuroevolution method leads to better algorithm performance and increases the robustness of evolved solutions against detrimental mutations
Integrating Evolutionary Computation with Neural Networks
There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimization of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniques. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents a solution of a classical problem using neural computing and evolutionary computing technique
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
Forcing neurocontrollers to exploit sensory symmetry through hard-wired modularity in the game of Cellz
Several attempts have been made in the past to construct encoding schemes that allow modularity to emerge in evolving systems, but success is limited. We believe that in order to create successful and scalable encodings for emerging modularity, we first need to explore the benefits of different types of modularity by hard-wiring these into evolvable systems. In this paper we explore different ways of exploiting sensory symmetry inherent in the agent in the simple game Cellz by evolving symmetrically identical modules. It is concluded that significant increases in both speed of evolution and final fitness can be achieved relative to monolithic controllers. Furthermore, we show that a simple function approximation task that exhibits sensory symmetry can be used as a quick approximate measure of the utility of an encoding scheme for the more complex game-playing task
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Modular feature selection using relative importance factors
Feature selection plays an important role in finding relevant or irrelevant features in classification. Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. In this paper, we explore the use of feature selection in modular GA-based classification. We propose a new feature selection technique, Relative Importance Factor (RIF), to find irrelevant features in the feature space of each module. By removing these features, we aim to improve classification accuracy and reduce the dimensionality of classification problems. Benchmark classification data sets are used to evaluate the proposed approaches. The experiment results show that RIF can be used to determine irrelevant features and help achieve higher classification accuracy with the feature space dimension reduced. The complexity of the resulting rule sets is also reduced which means the modular classifiers with irrelevant features removed will be able to classify data with a higher throughput
Feature selection for modular GA-based classification
Genetic algorithms (GAs) have been used as conventional methods for classifiers to adaptively evolve solutions for classification problems. Feature selection plays an important role in finding relevant features in classification. In this paper, feature selection is explored with modular GA-based classification. A new feature selection technique, Relative Importance Factor (RIF), is proposed to find less relevant features in the input domain of each class module. By removing these features, it is aimed to reduce the classification error and dimensionality of classification problems. Benchmark classification data sets are used to evaluate the proposed approach. The experiment results show that RIF can be used to find less relevant features and help achieve lower classification error with the feature space dimension reduced
The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System
Natural evolution has produced a tremendous diversity of functional
organisms. Many believe an essential component of this process was the
evolution of evolvability, whereby evolution speeds up its ability to innovate
by generating a more adaptive pool of offspring. One hypothesized mechanism for
evolvability is developmental canalization, wherein certain dimensions of
variation become more likely to be traversed and others are prevented from
being explored (e.g. offspring tend to have similarly sized legs, and mutations
affect the length of both legs, not each leg individually). While ubiquitous in
nature, canalization almost never evolves in computational simulations of
evolution. Not only does that deprive us of in silico models in which to study
the evolution of evolvability, but it also raises the question of which
conditions give rise to this form of evolvability. Answering this question
would shed light on why such evolvability emerged naturally and could
accelerate engineering efforts to harness evolution to solve important
engineering challenges. In this paper we reveal a unique system in which
canalization did emerge in computational evolution. We document that genomes
entrench certain dimensions of variation that were frequently explored during
their evolutionary history. The genetic representation of these organisms also
evolved to be highly modular and hierarchical, and we show that these
organizational properties correlate with increased fitness. Interestingly, the
type of computational evolutionary experiment that produced this evolvability
was very different from traditional digital evolution in that there was no
objective, suggesting that open-ended, divergent evolutionary processes may be
necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi
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
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