We present an approach to diversity maintenance based on separating the population into buckets based on similarity and biasing selection to keep individuals from all buckets in the population. We look at two approach...
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ISBN:
(纸本)9781450349390
We present an approach to diversity maintenance based on separating the population into buckets based on similarity and biasing selection to keep individuals from all buckets in the population. We look at two approaches to bucketing. The first uses a locally sensitive bucketing function on individuals. The second uses the K-Means clustering algorithms to divide the population. We focus our research on a family of deceptive problem domains which we dub Tricky Keys and analyze how the using bucketing methods changes evolutionary search results for problem instances of varying difficulty. Our results show that both bucketing by function and bucketing by clustering methods show an increase in probability of finding a good solution and in number of good solutions found.
Inference of the biochemical systems (BSs) via experimental data is important for understanding how biochemical components in vivo interact with each other. However, it is not a trivial task because BSs usually functi...
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Inference of the biochemical systems (BSs) via experimental data is important for understanding how biochemical components in vivo interact with each other. However, it is not a trivial task because BSs usually function with complex and nonlinear dynamics. As a popular ordinary equation (ODE) model, the S-System describes the dynamical properties of BSs by incorporating the power rule of biochemical reactions but behaves as a challenge because it has a lot of parameters to be confirmed. This work is dedicated to proposing a general method for inference of S-Systems by experimental data, using a biobjective optimization (BOO) model and a specially mixed-variable multiobjective evolutionary algorithm (mv-MOEA). Regarding that BSs are sparse in common sense, we introduce binary variables indicating network connections to eliminate the difficulty of threshold presetting and take data fitting error and the L-0-norm as two objectives to be minimized in the BOO model. Then, a selection procedure that automatically runs tradeoff between two objectives is employed to choose final inference results from the obtained nondominated solutions of the mv-MOEA. Inference results of the investigated networks demonstrate that our method can identify their dynamical properties well, although the automatic selection procedure sometimes ignores some weak connections in BSs.
Procedural Content Generation (PCG) for Games is a field that in the past few years has seen both extensive academic study and practical use in the games industry. One of its common uses being the generation of maps f...
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ISBN:
(纸本)9781538648469
Procedural Content Generation (PCG) for Games is a field that in the past few years has seen both extensive academic study and practical use in the games industry. One of its common uses being the generation of maps for levels within games that rely on replayability. While Cellular Automata is a PCG technique widely used for the creation of minor graphical systems, it has not yet seen much practical use in the generation of levels, part due to its inherent stochastic nature. With the purpose of presenting a malleable approach for improving levels created through Cellular Automata, this work presents a methodology that guides the generation process through the use of fractals, specifically Space-filling Curves. The product Automata of this process are implemented and polished on the Unity game engine, as to present their potential for generating procedural levels. Results show that this methodology can be used for the generation of organic, cohesive game levels.
For Sonar Automatic Target Recognition problems, the number of mine-like objects is relatively small compared to the number non-mine-like objects available. This creates a heavy bias towards non-mine-like objects and ...
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ISBN:
(数字)9781510608665
ISBN:
(纸本)9781510608665
For Sonar Automatic Target Recognition problems, the number of mine-like objects is relatively small compared to the number non-mine-like objects available. This creates a heavy bias towards non-mine-like objects and increases the processing resources needed for classifier training. In order to reduce resource needs and the bias towards non-mine-like objects, we investigate selection methods for reducing the non-mine-like target samples while still maintaining as much of the original training information as possible. Specifically, we investigate methods for reducing sample size and bias while maintaining good classifier performance. Several methods are considered during this investigation that cover a wide range of techniques, including clustering and evolutionary algorithms. Each method is evaluated based on the classifier performance when trained on the chosen data samples and the execution time to select the new training set. Results on each method tested are presented using sonar data collected using a sidescan sonar system.
Bilevel and multi-objective optimization methods are often useful to spatially target agri-environmental policy throughout a watershed. This type of problem is complex and is comprised of a number of practicalities: (...
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ISBN:
(纸本)9781450349208
Bilevel and multi-objective optimization methods are often useful to spatially target agri-environmental policy throughout a watershed. This type of problem is complex and is comprised of a number of practicalities: (i) a large number of decision variables, (ii) at least two inter-dependent levels of optimization between policy makers and policy followers, and (iii) uncertainty in decision variables and problem parameters. Given agricultural and economic data from the Raccoon watershed in central Iowa, we formulate a bilevel multi-objective optimization problem that accommodates objectives of both policy makers and farmers. The solution procedure then explicitly accounts for the nested nature of farm-level management decisions in response to agri-environmental policy incentives constructed by policy makers. We specifically examine the spatial targeting of a fertilizer-reduction incentive policy while seeking to maximize farm-level productivity while generating mandated water quality improvements using this framework. We test three different evolutionary optimization algorithms - m-BLEAQ, NSGA-II, and SPEA2 and show that m-BLEAQ is well suited for handling the bilevel optimization problems and the considered practicalities.
The availability of a model to measure the performance of evolutionary algorithms is very important, especially when these algorithms are applied to solve problems with high computational requirements. That model woul...
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The availability of a model to measure the performance of evolutionary algorithms is very important, especially when these algorithms are applied to solve problems with high computational requirements. That model would compute an index of the quality of the solution reached by the algorithm as a function of run-time. Conversely, if we fix an index of quality for the solution, the model would give the number of iterations to be expected. In this work, we develop a statistical model to describe the performance of PBIL and CHC evolutionary algorithms applied to solve the root identification problem. This problem is basic in constraint-based, geometric parametric modeling, as an instance of general constraint-satisfaction problems. The performance model is empirically validated over a benchmark with very large search spaces.
evolutionary algorithms are optimization methods inspired by natural evolution. They usually search for the optimal solution in large space areas. In evolutionary algorithms it is very important to select an appropria...
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ISBN:
(纸本)9788394625375
evolutionary algorithms are optimization methods inspired by natural evolution. They usually search for the optimal solution in large space areas. In evolutionary algorithms it is very important to select an appropriate balance between the ability of the algorithm to explore and exploit the search space. The paper presents a hybrid system consisting of a Genetic Algorithm and an evolutionary Strategy designed to optimize the function of many variables. In this system, we combined the ability of the Genetic Algorithm to explore the search space and the ability of the evolutionary Strategy to exploit the search space. Optimization performed by the Genetic Algorithm and the evolutionary Strategy runs at the same time, so it is possible to perform parallel computations. The results of the experiments suggest that the proposed system can be an effective tool in solving complex optimization problems.
Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of ...
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ISBN:
(纸本)9781509047802
Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of potential solutions in the given search space to obtain an optimal solution. In this paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been proposed that introduces a novel strategy to generate perturbations effectively. This strategy allows the individual to span across more possible solutions and thus, is able to produce better solutions. A comprehensive analysis of OSOMA on multi-dimensional unconstrained benchmark test functions is performed. OSOMA is then applied to solve real-time Dynamic Traveling Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and simulated using real-time data from Google Maps with a varying cost-metric between any two cities. Although DTSP is a very common and intuitive model in the real world, its presence in literature is still very limited. OSOMA performs exceptionally well on the problems mentioned above. To substantiate this claim, the performance of OSOMA is compared with SOMA, Differential Evolution and Particle Swarm Optimization.
Bio-hybrid systems made of robots and animals can be useful tools both for biology and robotics. To socially integrate robots into animal groups the robots should behave in a biomimetic manner with close loop interact...
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ISBN:
(数字)9783319635378
ISBN:
(纸本)9783319635361;9783319635378
Bio-hybrid systems made of robots and animals can be useful tools both for biology and robotics. To socially integrate robots into animal groups the robots should behave in a biomimetic manner with close loop interactions between robots and animals. Behavioural zebrafish experiments show that their individual behaviours depend on social interactions producing collective behaviour and depend on their position in the environment. Based on those observations we build a multilevel model to describe the zebrafish collective behaviours in a structured environment. Here, we present this new model segmented in spatial zones that each corresponds to different behavioural patterns. We automatically fit the model parameters for each zone to experimental data using a multi-objective evolutionary algorithm. We then evaluate how the resulting calibrated model compares to the experimental data. The model is used to drive the behaviour of a robot that has to integrate socially in a group of zebrafish. We show experimentally that a biomimetic multilevel and context-dependent model allows good social integration of fish and robots in a structured environment.
This paper presents a new Modified Fruit Fly Optimization Algorithm (MFOA) which is used to find the optimal PID controllers parameters applied to control a two-link robotic manipulator. The proposed new distribution ...
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ISBN:
(纸本)9781538621349
This paper presents a new Modified Fruit Fly Optimization Algorithm (MFOA) which is used to find the optimal PID controllers parameters applied to control a two-link robotic manipulator. The proposed new distribution law in MFOA for some of the fruit flies improves searching diversity in earlier iterations and increases solution precession in last iterations. In order to apply the PID controllers to the robot manipulator, a nonlinear feedback linearization control technique is employed which can fully linearize and decouple nonlinear robot's dynamics. Simulation results confirm that the MFOA-PID controller can achieve better closed-loop system responses with respect to the original FOA-PID controller.
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