Novelty Search in Competitive Coevolution

One of the main motivations for the use of competitive coevolution systems is their ability to capitalise on arms races between competing species to evolve increasingly sophisticated solutions. Such arms races can, however, be hard to sustain, and it has been shown that the competing species often converge prematurely to certain classes of behaviours. In this paper, we investigate if and how novelty search, an evolutionary technique driven by behavioural novelty, can overcome convergence in coevolution. We propose three methods for applying novelty search to coevolutionary systems with two species: (i) score both populations according to behavioural novelty; (ii) score one population according to novelty, and the other according to fitness; and (iii) score both populations with a combination of novelty and fitness. We evaluate the methods in a predator-prey pursuit task. Our results show that novelty-based approaches can evolve a significantly more diverse set of solutions, when compared to traditional fitness-based coevolution.Comment: To appear in 13th International Conference on Parallel Problem Solving from Nature (PPSN 2014

Coevolution of Firm Capabilities and Industry Competition

This paper proposes that rival firms not only search for new capabilities within their organization, but also for those that rest in their competitive environment. An integrated analysis of these search processes at both firm and industry levels of analysis shows how their interaction makes industries and firms coevolve over time. To contribute to an enhanced understanding of the concept of coevolution, a dynamic and integrative framework crossing meso and micro levels of analysis is constructed. This framework is applied to a longitudinal study of the music industry with a time-span of 120 years. The first part, a historical study, covers the period 1877 - 1990. The second part, a multiple-case study, covers the period 1990 - 1997. We conclude that search behavior drives coevolution through competitive dynamics among new entrants and incumbent firms and manifests itself in the simultaneous emergence of new business models and new organizational forms.coevolution;competitive regime;longitudinal research;multilevel research;music industry

Uniform coevolution for solving the density classification problem in cellular automata

Genetic and Evolutionary Computation Conference (GECCO 2000). Las Vegas, Nevada (USA), July 8-12 2000.Uniform Coevolution is based on competitive evolution ideas where the solution and example sets are evolving by means of a competition to generate difficult test beds for the solutions in a gradual way. The method has been tested with the density parity problem in cellular automata, where the selected examples can biased the solutions founded. The results show a high value of generality using Uniform coevolution, compared with no Co-evolutive approaches.Publicad

Multi - island competitive cooperative coevolution for real parameter global optimization

Problem decomposition is an important attribute of cooperative coevolution that depends on the nature of the problems in terms of separability which is defined by the level of interaction amongst decision variables. Recent work in cooperative coevolution featured competition and collaboration of problem decomposition methods that was implemented as islands in a method known as competitive island cooperative coevolution (CICC). In this paper, a multi-island competitive cooperative coevolution algorithm (MICCC) is proposed in which several different problem decomposition strategies are given a chance to compete, collaborate and motivate other islands while converging to a common solution. The performance of MICCC is evaluated on eight different benchmark functions and are compared with CICC where only two islands were utilized. The results from the experimental analysis show that competition and collaboration of several different island can yield solutions with a quality better than the two-island competition algorithm (CICC) on most complex multi-modal problems

Coevolutive adaptation of fitness landscape for solving the testing problem

IEEE International Conference on Systems, Man, and Cybernetics. Nashville, TN, 8-11 October 2000A general framework, called Uniform Coevolution, is introduced to overcome the testing problem in evolutionary computation methods. This framework is based on competitive evolution ideas where the solution and example sets are evolving by means of a competition to generate difficult test beds for the solutions in a gradual way. The method has been tested with two different problems: the robot navigation problem and the density parity problem in cellular automata. In both test cases using evolutive methods, the examples used in the learning process biased the solutions found. The main characteristics of the Uniform Coevolution method are that it smoothes the fitness landscape and, that it obtains “ideal learner examples”. Results using uniform coevolution show a high value of generality, compared with non co-evolutive approaches

Autonomous virulence adaptation improves coevolutionary optimization

A novel approach for the autonomous virulence adaptation (AVA) of competing populations in a coevolutionary optimization framework is presented. Previous work has demonstrated that setting an appropriate virulence, v, of populations accelerates coevolutionary optimization by avoiding detrimental periods of disengagement. However, since the likelihood of disengagement varies both between systems and over time, choosing the ideal value of v is problematic. The AVA technique presented here uses a machine learning approach to continuously tune v as system engagement varies. In a simple, abstract domain, AVA is shown to successfully adapt to the most productive values of v. Further experiments, in more complex domains of sorting networks and maze navigation, demonstrate AVA's efficiency over reduced virulence and the layered Pareto coevolutionary archive.A novel approach for the autonomous virulence adaptation (AVA) of competing populations in a coevolutionary optimization framework is presented. Previous work has demonstrated that setting an appropriate virulence, v, of populations accelerates coevolutionary optimization by avoiding detrimental periods of disengagement. However, since the likelihood of disengagement varies both between systems and over time, choosing the ideal value of v is problematic. The AVA technique presented here uses a machine learning approach to continuously tune v as system engagement varies. In a simple, abstract domain, AVA is shown to successfully adapt to the most productive values of v. Further experiments, in more complex domains of sorting networks and maze navigation, demonstrate AVA's efficiency over reduced virulence and the layered Pareto coevolutionary archive

Evidence of coevolution in multi-objective evolutionary algorithms

This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking allow for a form of coevolutionary dynamics that is observed when 1) changes are made in what solutions are able to interact during the ranking process and 2) evolution takes place in a multi-objective environment. This research contributes to the study of simulated evolution in a at least two ways. First, it establishes a broader relationship between coevolution and multi-objective optimization than has been previously considered in the literature. Second, it demonstrates that the preconditions for coevolutionary behavior are weaker than previously thought. In particular, our model indicates that direct cooperation or competition between species is not required for coevolution to take place. Moreover, our experiments provide evidence that environmental perturbations can drive coevolutionary processes; a conclusion that mirrors arguments put forth in dual phase evolution theory. In the discussion, we briefly consider how our results may shed light onto this and other recent theories of evolution

Resource Sharing and Coevolution in Evolving Cellular Automata

Evolving one-dimensional cellular automata (CAs) with genetic algorithms has provided insight into how improved performance on a task requiring global coordination emerges when only local interactions are possible. Two approaches that can affect the search efficiency of the genetic algorithm are coevolution, in which a population of problems---in our case, initial configurations of the CA lattice---evolves along with the population of CAs; and resource sharing, in which a greater proportion of a limited fitness resource is assigned to those CAs which correctly solve problems that fewer other CAs in the population can solve. Here we present evidence that, in contrast to what has been suggested elsewhere, the improvements observed when both techniques are used together depend largely on resource sharing alone.Comment: 8 pages, 1 figure; http://www.santafe.edu/~evca/rsc.ps.g

Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior

In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad