5,487 research outputs found
Cultural Learning in a Dynamic Environment: an Analysis of Both Fitness and Diversity in Populations of Neural Network Agents
Evolutionary learning is a learning model that can be described as the iterative Darwinian process of fitness-based selection and genetic transfer of information leading to populations of higher fitness. Cultural learning describes the process of information transfer between individuals in a population through non-genetic means. Cultural learning has been simulated by combining genetic algorithms and neural networks using a teacher/pupil scenario where highly fit individuals are selected as teachers and instruct the next generation. This paper examines the effects of cultural learning on the evolutionary process of a population of neural networks. In particular, the paper examines the genotypic and phenotypic diversity of a population as well as its fitness. Using these measurements, it is possible to examine the effects of cultural learning on the population's genetic makeup. Furthermore, the paper examines whether cultural learning provides a more robust learning mechanism in the face of environmental changes. Three benchmark tasks have been chosen as the evolutionary task for the population: the bit-parity problem, the game of tic-tac-toe and the game of connect-four. Experiments are conducted with populations employing evolutionary learning alone and populations combining evolutionary and cultural learning in an environment that changes dramatically.Cultural Learning, Dynamic Environments, Diversity, Multi-Agent Systems, Artificial Life
Open-Ended Evolutionary Robotics: an Information Theoretic Approach
This paper is concerned with designing self-driven fitness functions for
Embedded Evolutionary Robotics. The proposed approach considers the entropy of
the sensori-motor stream generated by the robot controller. This entropy is
computed using unsupervised learning; its maximization, achieved by an on-board
evolutionary algorithm, implements a "curiosity instinct", favouring
controllers visiting many diverse sensori-motor states (sms). Further, the set
of sms discovered by an individual can be transmitted to its offspring, making
a cultural evolution mode possible. Cumulative entropy (computed from ancestors
and current individual visits to the sms) defines another self-driven fitness;
its optimization implements a "discovery instinct", as it favours controllers
visiting new or rare sensori-motor states. Empirical results on the benchmark
problems proposed by Lehman and Stanley (2008) comparatively demonstrate the
merits of the approach
Evolution of Swarm Robotics Systems with Novelty Search
Novelty search is a recent artificial evolution technique that challenges
traditional evolutionary approaches. In novelty search, solutions are rewarded
based on their novelty, rather than their quality with respect to a predefined
objective. The lack of a predefined objective precludes premature convergence
caused by a deceptive fitness function. In this paper, we apply novelty search
combined with NEAT to the evolution of neural controllers for homogeneous
swarms of robots. Our empirical study is conducted in simulation, and we use a
common swarm robotics task - aggregation, and a more challenging task - sharing
of an energy recharging station. Our results show that novelty search is
unaffected by deception, is notably effective in bootstrapping the evolution,
can find solutions with lower complexity than fitness-based evolution, and can
find a broad diversity of solutions for the same task. Even in non-deceptive
setups, novelty search achieves solution qualities similar to those obtained in
traditional fitness-based evolution. Our study also encompasses variants of
novelty search that work in concert with fitness-based evolution to combine the
exploratory character of novelty search with the exploitatory character of
objective-based evolution. We show that these variants can further improve the
performance of novelty search. Overall, our study shows that novelty search is
a promising alternative for the evolution of controllers for robotic swarms.Comment: To appear in Swarm Intelligence (2013), ANTS Special Issue. The final
publication will be available at link.springer.co
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
A tractable genotype-phenotype map for the self-assembly of protein quaternary structure
The mapping between biological genotypes and phenotypes is central to the
study of biological evolution. Here we introduce a rich, intuitive, and
biologically realistic genotype-phenotype (GP) map, that serves as a model of
self-assembling biological structures, such as protein complexes, and remains
computationally and analytically tractable. Our GP map arises naturally from
the self-assembly of polyomino structures on a 2D lattice and exhibits a number
of properties: (genotypes vastly outnumber phenotypes),
(genotypic redundancy varies greatly between
phenotypes), (phenotypes consist
of disconnected mutational networks) and (most
phenotypes can be reached in a small number of mutations). We also show that
the mutational robustness of phenotypes scales very roughly logarithmically
with phenotype redundancy and is positively correlated with phenotypic
evolvability. Although our GP map describes the assembly of disconnected
objects, it shares many properties with other popular GP maps for connected
units, such as models for RNA secondary structure or the HP lattice model for
protein tertiary structure. The remarkable fact that these important properties
similarly emerge from such different models suggests the possibility that
universal features underlie a much wider class of biologically realistic GP
maps.Comment: 12 pages, 6 figure
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
Statistical Physics of Evolutionary Trajectories on Fitness Landscapes
Random walks on multidimensional nonlinear landscapes are of interest in many
areas of science and engineering. In particular, properties of adaptive
trajectories on fitness landscapes determine population fates and thus play a
central role in evolutionary theory. The topography of fitness landscapes and
its effect on evolutionary dynamics have been extensively studied in the
literature. We will survey the current research knowledge in this field,
focusing on a recently developed systematic approach to characterizing path
lengths, mean first-passage times, and other statistics of the path ensemble.
This approach, based on general techniques from statistical physics, is
applicable to landscapes of arbitrary complexity and structure. It is
especially well-suited to quantifying the diversity of stochastic trajectories
and repeatability of evolutionary events. We demonstrate this methodology using
a biophysical model of protein evolution that describes how proteins maintain
stability while evolving new functions
Fitness sharing and niching methods revisited
Interest in multimodal optimization function is expanding rapidly since real-world optimization problems often require the location of multiple optima in the search space. In this context, fitness sharing has been used widely to maintain population diversity and permit the investigation of many peaks in the feasible domain. This paper reviews various strategies of sharing and proposes new recombination schemes to improve its efficiency. Some empirical results are presented for high and a limited number of fitness function evaluations. Finally, the study
compares the sharing method with other niching techniques
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