Clarifying the Difference in Local Optima Network Sampling Algorithms

We conduct the first ever statistical comparison between two Local Optima Network (LON) sampling algorithms. These methodologies attempt to capture the connectivity in the local optima space of a fitness landscape. One sampling algorithm is based on a random-walk snowballing procedure, while the other is centred around multiple traced runs of an Iterated Local Search. Both of these are proposed for the Quadratic Assignment Problem (QAP), making this the focus of our study. It is important to note the sampling algorithm frameworks could easily be modified for other domains. In our study descriptive statistics for the obtained search space samples are contrasted and commented on. The LON features are also used in linear mixed models and random forest regression for predicting heuristic optimisation performance of two prominent heuristics for the QAP on the underlying combinatorial problems. The model results are then used to make deductions about the sampling algorithms’ utility. We also propose a specific set of LON metrics for use in future predictive models alongside previously-proposed network metrics, demonstrating the payoff in doing so

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

Braitenberg Vehicles as Developmental Neurosimulation

The connection between brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. Particularly in artificial intelligence research, behavior is generated by a black box approximating the brain. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. This model generates outputs and behaviors from a priori associations, yet this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We will introduce our approach, which is to use Braitenberg Vehicles (BVs) to model the development of an artificial nervous system. The resulting developmental BVs will generate behaviors that range from stimulus responses to group behavior that resembles collective motion. Next, we will situate this work in the domain of artificial brain networks. Then we will focus on broader themes such as embodied cognition, feedback, and emergence. Our perspective will then be exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we will revisit concepts related to our approach and how they might guide future development.Comment: 32 pages, 8 figures, 2 table

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

EvoEvo Deliverable 6.8: Final report

Final report of the EvoEvo project

New genetic algorithms for constrained optimisation and applications to design of composite laminates

A general purpose constraint handling technique for genetic algorithms (GA) is developed by borrowing principles from multi-objective optimisation. This is in view of the many issues still facing constraint handling in GA, particularly in the number of control parameters that overwhelms the user, as well as other GA parameters, which are currently lacking in heuristics to guide successful implementations. Constraints may be handled as individual objectives of decreasing priorities or by a weighted-sum measurement of normalised violation, as would be done in multi-objective scenarios, with full consideration of the main cost function. Rather than the unnecessary specialisation seen in many new heuristics proposed for GA, the simplicity, generality and flexibility of the technique is maintained, where several options such as partial or full constraint evaluation, tangible or Pareto-ranked fitness, and implicit dominance evaluation are presented. By reducing the number of constraint evaluations, these options increase the probability of discovering optimal regions, and hence increase GA efficiency. Studies in applications to a constrained numerical problem, and to the design of realistic composite laminate plates and structures, serve to demonstrate the ease of implementation and general reliability in heavily constrained problems. The difference in the dynamics of partial or full violation knowledge showed that while the former reduced the overall number of constraint evaluations performed, the latter compromises for the expense of full constraint evaluations in the reduced number of GA generations, whether in terms of discovering feasible regions or optimal solutions. The benefit of partial or full constraint evaluations is subjective, as it ultimately depends on the trade-off in the computational cost of constraint evaluations and GA search