Uncovering the physics of flapping flat plates with artificial evolution

We consider an experiment in which a rectangular flat plate is flapped with two degrees of freedom, and a genetic algorithm tunes its trajectory parameters so as to achieve maximum average lift force, thus evolving a population of trajectories all yielding optimal lift forces. We cluster the converged population by defining a dynamical formation number for a flapping flat plate, thus showing that optimal unsteady force generation is linked to the formation of a leading-edge vortex with maximum circulation. Force and digital particle image velocimetry measurements confirm this result

First Steps Towards a Runtime Comparison of Natural and Artificial Evolution

Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse their runtime on many illustrative problems. Here we apply this theory to a simple model of natural evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between occurrence of new mutations is much longer than the time it takes for a new beneficial mutation to take over the population. In this situation, the population only contains copies of one genotype and evolution can be modelled as a (1+1)-type process where the probability of accepting a new genotype (improvements or worsenings) depends on the change in fitness. We present an initial runtime analysis of SSWM, quantifying its performance for various parameters and investigating differences to the (1+1)EA. We show that SSWM can have a moderate advantage over the (1+1)EA at crossing fitness valleys and study an example where SSWM outperforms the (1+1)EA by taking advantage of information on the fitness gradient

Optimising the topology of complex neural networks

In this paper, we study instances of complex neural networks, i.e. neural netwo rks with complex topologies. We use Self-Organizing Map neural networks whose n eighbourhood relationships are defined by a complex network, to classify handwr itten digits. We show that topology has a small impact on performance and robus tness to neuron failures, at least at long learning times. Performance may howe ver be increased (by almost 10%) by artificial evolution of the network topo logy. In our experimental conditions, the evolved networks are more random than their parents, but display a more heterogeneous degree distribution

DAE: Planning as Artificial Evolution -- (Deterministic part)

International audienceThe sub-optimal DAE planner implements the stochastic approach for domain-independent planning decomposition. The purpose of this planner is to optimize the makespan, or the number of actions, by generating ordered sequences of intermediate goals via a process of artificial evolution. For the evolutionary part we used the Evolving Objects (EO) library, and to solve each intermediate subproblem we used the constraint-based optimal temporal planner CPT. Therefore DAE can only solve problems that CPT can solve. Compression of subplans into a global solution plan is also achieved efficiently with CPT by exploiting causalities found so far. Because the selection of predicates for intermediate goal generation is still an open question, we have submitted two planners DAE1 and DAE2 that use different strategies for the generation of intermediate goals. An empirical formula has been defined to set a limit on the number of backtracks allowed for solving the intermediate subproblems

Towards the Evolution of Novel Vertical-Axis Wind Turbines

Renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world's energy supply mix, but still remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under approximated wind tunnel conditions. An artificial neural network is used as a surrogate model to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency, resulting in an important cost reduction. Unlike in other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.Comment: 14 pages, 11 figure

Evolving collective behavior in an artificial ecology

Collective behavior refers to coordinated group motion, common to many animals. The dynamics of a group can be seen as a distributed model, each “animal” applying the same rule set. This study investigates the use of evolved sensory controllers to produce schooling behavior. A set of artificial creatures “live” in an artificial world with hazards and food. Each creature has a simple artificial neural network brain that controls movement in different situations. A chromosome encodes the network structure and weights, which may be combined using artificial evolution with another chromosome, if a creature should choose to mate. Prey and predators coevolve without an explicit fitness function for schooling to produce sophisticated, nondeterministic, behavior. The work highlights the role of species’ physiology in understanding behavior and the role of the environment in encouraging the development of sensory systems

Designing High Thermal Conductive Materials Using Artificial Evolution

There is a growing need for efficient and effective methods of heat dissipation. One driving force for this need is computer processors. As the processor grows faster and more powerful, it requires more electricity to perform tasks which results in high amounts of heat generated. Designing materials with high thermal-conductivity can enable heat dissipation to allow faster and more powerful computers. Creating such materials is often a trial-and-error process by which several material composites are tested for desirable thermal conductivity. In this research, we employed the use of a genetic algorithm, which mimics the process of evolution through natural selection, as an alternative to exhaustive trial-and-error approaches to help design a graphene based template material with high thermal conductivity. The algorithm creates a population of randomly generated configurations, then uses an open source physics (molecular dynamics) simulator, LAMMPS, linked with High-Performance Computing to run a molecular dynamics simulation for each composite to derive a fitness, or score for the material. The highest scoring materials undergo crossover to create offspring for the next generation. Over time, these algorithms have the potential to find a composite with desirable conductivity through this pseudo-evolution process