Large-scale optimization : combining co-operative coevolution and fitness inheritance

Large-scale optimization, here referring mainly to problems with many design parameters remains a serious challenge for optimization algorithms. When the problem at hand does not succumb to analytical treatment (an overwhelmingly common place situation), the engineering and adaptation of stochastic black box optimization methods tends to be a favoured approach, particularly the use of Evolutionary Algorithms (EAs). In this context, many approaches are currently under investigation for accelerating performance on large-scale problems, and we focus on two of those in this research. The first is co-operative co-evolution (CC), where the strategy is to successively optimize only subsets of the design parameters at a time, keeping the remainder fixed, with an organized approach to managing and reconciling these subspace optimization. The second is fitness inheritance (FI), which is essentially a very simple surrogate model strategy, in which, with some probability, the fitness of a solution is simply guessed to be a simple function of the finesses of that solution’s parents. Both CC and FI have been found successful on nontrivial and multiple test cases, and they use fundamentally distinct strategies. In this thesis, we explored the extent to which both of these strategies can be used to provide additional benefits. In addition to combining CC and FI, this thesis also introduces a new FI scheme which further improves the performance of CC-FI. We show that the new algorithm CC-FI is highly effective for solving problems, especially when the new FI scheme is used. In the thesis, we also explored two basic adaptive parameter setting strategies for the FI component. We found that engineering FI (and CC, where it was otherwise not present) into these algorithms led to good performance and results

A modified dual-population approach for solving multi-objective problems

Maintaining the balance between convergence and diversity plays a vital role in multi-objective evolutionary algorithms (MOEAs). However, most MOEAs cannot reach a satisfying balance, especially when solving problems having complicated pareto optimal sets. In this paper, we present a modified cooperative co-evolution approach for achieving better convergence and diversity simultaneously (namely DPP2). In DPP2, while populations are trying to achieve both criteria, the priority being set for these criteria will be different. One population focuses on achieving better convergence (by using pareto-based ranking scheme), while the other is for ensuring the population diversity (by using the decomposition-based method). After that, we use a cooperation mechanism to integrate the two populations and create a new combined population with hopes of having both characteristics (i.e. converged and diverse). Performance of DPP2 is examined on the well-known benchmarks of multiobjective optimization problems (MOPs) using the hypervolume (HV), the generational distance (GD), the inverted generational distance (IGD) metrics. In comparison with the original version DPP algorithm, experimental results indicated that DPP2 can significantly outperform DPP on the benchmark problems with stable results

Mindset: The 2.5D Platformer

Mindset is a 2.5D platformer video game developed in Unreal Engine. The player must navigate different levels and overcome various challenges on a quest to reach the end of the game. Each level of Mindset is made to represent a different emotion in the protagonist’s life such as contentment, anger, and sadness. Part of the core functionality of the game is this idea that there are two dimensions to every level, a foreground and a background. The challenges in each level incorporate the core mechanic of the game known as “plane shifting” in which the player swaps from foreground to background or vice versa. The challenges in each level revolve around this idea of plane shifting, and it is up to the player to figure out how to solve them

Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

© Peter C. R. Lane, Fernand Gobet. This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY-NC 3.0)Creating robust, reproducible and optimal computational models is a key challenge for theorists in many sciences. Psychology and cognitive science face particular challenges as large amounts of data are collected and many models are not amenable to analytical techniques for calculating parameter sets. Particular problems are to locate the full range of acceptable model parameters for a given dataset, and to confirm the consistency of model parameters across different datasets. Resolving these problems will provide a better understanding of the behaviour of computational models, and so support the development of general and robust models. In this article, we address these problems using evolutionary algorithms to develop parameters for computational models against multiple sets of experimental data; in particular, we propose the ‘speciated non-dominated sorting genetic algorithm’ for evolving models in several theories. We discuss the problem of developing a model of categorisation using twenty-nine sets of data and models drawn from four different theories. We find that the evolutionary algorithms generate high quality models, adapted to provide a good fit to all available data.Peer reviewedFinal Published versio

Voxelisation in the 3-D Fly Algorithm for PET

International audienceThe Fly Algorithm was initially developed for 3-D robot vision applications. It consists in solving the inverse problem of shape reconstruction from projections by evolving a population of 3-D points in space (the 'flies'), using an evolutionary optimisation strategy. Here, in its version dedicated to tomographic reconstruction in medical imaging, the flies are mimicking radioactive photon sources. Evolution is controlled using a fitness function based on the discrepancy of the projections simulated by the flies with the actual pattern received by the sensors. The reconstructed radioactive concentration is derived from the population of flies, i.e. a collection of points in the 3-D Euclidean space, after convergence. 'Good' flies were previously binned into voxels. In this paper, we study which flies to include in the final solution and how this information can be sampled to provide more accurate datasets in a reduced computation time. We investigate the use of density fields, based on Metaballs and on Gaussian functions respectively, to obtain a realistic output. The spread of each Gaussian kernel is modulated in function of the corresponding fly fitness. The resulting volumes are compared with previous work in terms of normalised-cross correlation. In our test-cases, data fidelity increases by more than 10% when density fields are used instead of binning. Our method also provides reconstructions comparable to those obtained using well-established techniques used in medicine (filtered back-projection and ordered subset expectation-maximisation)

Road Profile Identification by Genetic Algorithm with Multistage Search using Vibration Response of a Quarter Vehicle Model

Vehicle performance is affected by road profile since the road profile supplies vibration to the vehicle. The road profile can be identified by vibration response of the vehicle. By using the vibration response, road profile identification can be, in fact, formulated as a single-objective optimization in which an objective, a minimization criterion, is the numerical difference between vehicle vibration response of actual road profile and that of predicted road profile. This paper present multistage search in genetic algorithm (GA), an optimization algorithm, in the road profile identification. In the multistage search, a solution is divided into a number of parts and each part is consequently evolved as GA process separately from other parts. A quarter vehicle models with two test cases of double bump on road profiles are used. Simulation runs reveal that the multistage search can enhance performance of GA. In addition, the multistage search using the least number of decision variables in each solution part gives the best results of the optimized solutions

Co-operative coevolution for computational creativity: a case study In videogame design

The term procedural content generation (PCG) refers to writing software which can synthesise content for a game (or other media such as film) without further intervention from a designer. PCG has become a rich area of research in recent years, finding new ways to apply artificial intelligence to generate high-quality game content such as levels, weapons or puzzles for games. Such research is generally constrained to a single type of content, however, with the assumption that the remainder of the game's design will be fixed by an external designer. Generating many aspects of a game's design simultaneously, perhaps ultimately generating the entirety of a game's design, using PCG is not a well-explored idea. The notion of automated game design is not well-established, and is not seen as a task distinct from simply performing lots of PCG tasks at the same time. In particular, the high-level design tasks guiding the creative direction of a game are all but completely absent in PCG literature, because it is rare that a designer wishes to hand over such responsibility to a PCG system. We present here ANGELINA, an automated game designer that has developed games using a multi-faceted approach to content generation underpinned by a co-operative co-evolutionary approach which breaks down a game design into several distinct tasks, each of which controlled by an evolutionary subsystem within ANGELINA. We will show that this approach works well to automate game design, can be ported across many game engines and game genres, and can be enhanced and extended using novel computational creativity techniques to give the system a heightened sense of autonomy and independence.Open Acces