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

Heterogeneity and Evolutionary Change - Empirical Conception, Findings and Unresolved Issues

In this position paper we deal with the conception of heterogeneity as both the force and the result of evolutionary change. We ask, how this heterogeneity can be measured empirically and how we can get a measure which allows to get a broad comparable empirical account especially on several levels of aggregation. Based on this discussion we suggest that for several questions the measures of total factor productivity (TFP) and local changes of TFP seem to be acceptable candidates for measuring heterogeneity and its dynamics. Examples out of a number of empirical investigations applying this measures show how interesting empirical facts about evolutionary change on several levels of aggregation can be detected. The paper concludes by raising a number of unresolved issues mainly related to the question about the relationship between evolutionary dynamics on several levels of aggregation.

Exploiting non-dominance in multi agent systems: An artificial immune algorithm for distributed and complex Problem solving environments

An ideal Multi Agent System is flat and has no dominant hierarchy. Multi agent computational and problem solving environments have been advocated for their ability to deliver overall solutions that are innovative and creative. There is however a significant threat to the coherence of Multi Agent Systems; chaos. This paper poses a new vision to the control and immunisation of the Multi Agent Systems against chaos. Employing a complexity measure of the problem and its lower and upper bounds, and monitoring the complexity of the problem solving agents’ interactions, we propose the holistic control of the Multi Agent Systems that leads to immunisation of the system against chaos. The control however is not central and appears in the form of the agents’ common knowledge and determines their tendency to proactively communicate

Computer aided electronic circuit design Status report, Dec. 1, 1967 - May 31, 1968

Computer aided electronic circuit desig

R&D Subsidization effect and network centralization. Evidence from an agent-based micro-policy simulation

This paper presents an agent-based micro-policy simulation model assessing public R&D policy effect when R&D and non-R&D performing companies are located within a network. We set out by illustrating the behavioural structure and the computational logic of the proposed model; then, we provide a simulation experiment where the pattern of the total level of R&D activated by a fixed amount of public support is analysed as function of companies’ network topology. More specifically, the suggested simulation experiment shows that a larger “hubness” of the network is more likely accompanied with a decreasing median of the aggregated total R&D performance of the system. Since the aggregated firm idiosyncratic R&D (i.e., the part of total R&D independent of spillovers) is slightly increasing, we conclude that positive cross-firm spillover effects - in the presence of a given amount of support - have a sizeable impact within less centralized networks, where fewer hubs emerge. This may question the common wisdom suggesting that larger R&D externality effects should be more likely to arise when few central champions receive a support

Enhancing the predictive performance of ensemble models through novel multi-objective strategies: evidence from credit risk and business model innovation survey data

This paper proposes novel multi-objective optimization strategies to develop a weighted ensemble model. The comparison of the performance of the proposed strategies against simulated data suggests that the multi-objective strategy based on joint entropy is superior to other proposed strategies. For the application, generalization, and practical implications of the proposed approaches, we implemented the model on two real datasets related to the prediction of credit risk default and the adoption of the innovative business model by firms. The scope of this paper can be extended in ordering the solutions of the proposed multi- objective strategies and can be generalized for other similar predictive task