Download this paper as: PDF lowell-alifea. Abstract Evolving swarms can be used both to solve real-world problems and to study biological and ecological phenomena. We simulated an evolving swarm of birds under three different types of climate-change-related environmental variation. We found that desertification increased expirations within the swarm and decreased population stability.
The direction of the variation. The environmental variation also affected the genetics of the birds, with decreased food availability leading to collision avoidance genes being downplayed, and searching behavior for food being changed. High-intensity environmental variation led to less genetic stability post-change than lower-intensity environmental variation. Download this paper as: PDF lowell-alifeb. Robot Coverage Control by Evolved Neuromodulation.
Prediction and Classification of Respiratory Motion
Abstract An important connection between evolution and learning was made over a century ago and is now termed as the Baldwin effect. Learning acts as a guide for an evolutionary search process.
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In this study reinforcement learning agents are trained to solve the robot coverage control problem. These agents are improved by evolving neuromodulatory gene regulatory networks GRN that influence the learning and memory of agents. Agents trained by these neuromodulatory GRNs can consistently generalize better than agents trained with fixed parameter settings. This work introduces evolutionary GRN models into the context of neuromodulation and illustrates some of the benefits that stem from neuromodulatory GRNs.
Download this paper as: PDF harringtonijcnn. Autoconstructive Evolution for Structural Problems. Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion , pp. Abstract While most hyper-heuristics search for a heuristic that is later used to solve classes of problems, autoconstructive evolution represents an alternative which simultaneously searches both heuristic and solution space. In this study we contrast autoconstructive evolution, in which intergenerational variation is accomplished by the evolving programs themselves, with a genetic programming system, PushGP, to understand the dynamics of this hybrid approach.
A problem size scaling analysis of these genetic programming techniques is performed on structural problems.
These problems involve fewer domain-specic features than most model problems while maintaining core features representative of program search. We use two such problems, Order and Majority, to study autoconstructive evolution in the Push programming language. Download this paper as: PDF harringtonautoconstruction. Computational Neuroecology of Communicated Somatic Markers. Abstract The somatic marker hypothesis offers a physiological basis for emotion.
Somatic markers are thought to stem from basic survival behaviors, and it has been hypothesized that emotional communication can increase the survival rate of a population. We investigate these neuroecological questions in predator-prey simulations by exploring the effect of communicated somatic markers on individuals and their ecology in order to establish an understanding of their evolvability.
In particular, we show how fear, happiness, and to a lesser extent surprise, can be favored by natural selection. Download this paper as: PDF harringtonneuroeco.
From evolutionary computation to the evolution of things
Abstract In the research described here we examine the emergence of signaling from non-communicative origins, using the Sir Philip Sidney Game as a framework for our analysis. This game is known to exhibit a number of interesting dynamics. In our study, we quantify the difficulty of reaching multiple types of equilibria from initially non-communicative populations with an infinite population model.
We then compare the ability of finite populations with typical tournament selection to approximate the behaviors observed in infinite populations. Our findings suggest that honest signaling equilibria are difficult to reach from non-communicative origins. In the second part of the paper, we show that the finite model fails to model dynamics that permit deceptive signaling under typical evolutionary conditions, where infinite populations exhibit spiraling behavior between honest and deceptive signaling.
Download this paper as: PDF harringtonsignaling. Abstract In the study described here we examine the importance of social tags in the emergence and maintenance of signaling, using the Sir Philip Sydney Game. We use tags in the calculation of inclusive tness for members in a nite population, and analyze their evolution under dierent population distributions. We support the claim that inclusive tness theory may not be sufficient to explain the evolution of cooperation.
While cooperativity through honest signaling is sometimes achieved with tag-based relatedness, we suggest that the importance of tag-based mechanisms may not simply be due to their role in kin selection. Download this paper as: PDF ozisiksignalingtags. Tag-based Modularity in Tree-based Genetic Programming.
Why We Do Not Evolve Software? Analysis of Evolutionary Algorithms
Proceedings of the Genetic and Evolutionary Computation Conference , pp. Abstract Several techniques have been developed for allowing genetic programming systems to produce programs that make use of subroutines, macros, and other modular program structures.
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- From evolutionary computation to the evolution of things | Nature.
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A recently proposed technique, based on the "tagging" and tag-based retrieval of blocks of code, has been shown to have novel and desirable features, but this was demonstrated only within the context of the PushGP genetic programming system. Following a suggestion in the GECCO publication on this technique we show here how tag-based modules can be incorporated into a more standard tree-based genetic programming system. We describe the technique in detail along with some possible extensions, outline arguments for its simplicity and potential power, and present results obtained using the technique on problems for which other modularization techniques have been shown to be useful.
The results are mixed; substantial benets are seen on the lawnmower problem but not on the Boolean evenparity problem. We discuss the observed results and directions for future research. Download this paper as: PDF spectortags. Abstract We use emotional communication within a predator-prey game to evaluate the tradeoff between socio-emotional behavior at individual- and species- scales.
In this predator-prey game, individual predators and prey use emotion in their decision making, and communicate their emotional state with neighboring conspecifics. The model of emotion is based upon the somatic marker hypothesis. In comparing individual utility and population dynamics we find emotion is capable of both supporting species and individual gain. Compositional Autoconstructive Dynamics.
Abstract Autoconstructive evolution is the idea of evolving programs through self-creation. USA , — Akey, J. Tracking footprints of artificial selection in the dog genome.
- IEEE Congress on Evolutionary Computation.
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Dennett, D. Darwin's Dangerous Idea Penguin, Goldberg, D. Fogel, D. Evolution and Optimum Seeking Wiley, Banzhaf, W. Genetic Programming: an Introduction Morgan Kaufmann, Storn, R. Differential evolution — a simple and efficient heuristic for global optimization over continuous spaces. Price, K. Kennedy, J. Particle swarm optimization. In Proc. Swarm Intelligence Morgan Kaufmann, Are genetic algorithms function optimizers? Hornby, G. Arias-Montano, A.
Multiobjective evolutionary algorithms in aeronautical and aerospace engineering. IEEE Trans. Besnard, J. Automated design of ligands to polypharmacological profiles. Nature , — Huyer, W. A comparison of global search algorithms for continuous black box optimization. Hansen, N. Completely derandomized self-adaptation in evolution strategies. This article introduced the CMA-ES algorithm, widely regarded as the state of the art in numerical optimization.
Contemporary Evolution Strategies Springer, Yao, X. Evolving artificial neural networks. IEEE 87 , — Fink Prize Paper Award, brought together different strands of research and drew attention to the potential benefits of combining these two forms of learning.
Floreano, D. Neuroevolution: from architectures to learning. Barros, R. A survey of evolutionary algorithms for decision-tree induction. Man Cybern. C 42 , — Widera, P. GP challenge: evolving energy function for protein structure prediction. Evolvable Mach. A combined machine learning and genetic algorithm approach to controller design.
Watson, R. Embodied evolution: distributing an evolutionary algorithm in a population of robots. Bredeche, N. Environment-driven distributed evolutionary adaptation in a population of autonomous robotic agents. Nolfi, S. Bongard, J. Evolutionary robotics. ACM 56 , 74—85 Evolution of adaptive behavior in robots by means of Darwinian selection. PLoS Biol.
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Hinton, G. How learning can guide evolution. Complex Syst. This seminal paper showed that learning can guide evolution even though characteristics acquired by the phenotype are not communicated to the genotype. Borenstein, E. The effect of phenotypic plasticity on evolution in multipeaked fitness landscapes. Paenke, I. Balancing population and individual level of adaptation in changing environments. Chen, X.
Multi-facet survey on memetic computation. Krasnogor, N. A tutorial for competent memetic algorithms: model, taxonomy and design issues. Smith, J. A genetic approach to statistical disclosure control. Bentley, P. Creative Evolutionary Systems Morgan Kaufmann, Romero, J. Secretan, J. Picbreeder: a case study in collaborative evolutionary exploration of design space.
Evolutionary Design by Computers Morgan Kaufmann, Hingston, P. Advances in Evolutionary Design Springer, Human-competitive results produced by genetic programming. Offers quantifiable definitions for human competitiveness and a well-documented overview of success stories, including the first patents thought to be granted to inventions created by artificial intelligence. Theory of evolutionary algorithms: a bird's eye view.
Wolpert, D. A sequential genetic algorithm proceeds in an iterative manner by generating new populations of string from the old ones. Every string is the encoded version of a tentative solution. An evaluation function associates a fitness measure to every string indicating its suitability to the problem. The algorithm applies stochastic operators such as selection, crossover and mutation on an initially random population in order to compute a whole generation of new strings.
Since GAs apply operations drawn from nature, the nomenclature used in this field is closely related to the terms we can find in biology. The next table summarizes the meaning of these special terms in the aim of helping novel researchers. Unlike most other optimization techniques, GAs maintain a population of tentative solution that are manipulated competitively by applying some variation operators to find a global optimum.
This characteristic although is very useful to escape from local optimum, requires high computational resources large memory and search times, for example , and thus a variety of are being studied to design efficient GAs. With this goal numerous advances are continuously being achieved by designing new operators, hybrid algorithms, and more. PGAs are not only parallel versions of sequential GAs. In fact they actually reach the ideal goal of having a parallel algorithm whose behavior is better than the sum of separate behaviors of its component sub-algorithms. The sequential genetic algorithm operates on a population of strings or, in general, structures of arbitrary complexity representing tentative solutions.
In textbook versions of GAs, every string is called an individual and it is composed of one or more chromosomes and a fitness value. Normally, an individual contains just one chromosome that represents the set of parameters called genes. Every gene is in turn encoded in usually binary by using a given number of alleles 0,1.
Figure 2. Some details on the genotype.