By studying the fitness landscape properties of engine calibration problem we propose a new Principal Component Analysis (PCA) based optimisation algorithm for the problem. The engine calibration problem in this paper...
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ISBN:
(纸本)9781509006229
By studying the fitness landscape properties of engine calibration problem we propose a new Principal Component Analysis (PCA) based optimisation algorithm for the problem. The engine calibration problem in this paper is to minimise the fuel consumption, gas emission and particle emission of a Jaguar car engine. To evaluate the fuel consumption and emissions of the engine, a model of the engine that was developed in University of Birmingham was used. A strength Pareto method is used to convert the three objectives into one fitness value. Then a local search algorithm is used to find local optima. We then study these local optima to find the properties of good solutions in the landscape. Our studies on the good solutions show that the best solutions in the landscape show some patterns. We perform Principal Component Analysis (PCA) on the good solutions and show that these components present certain properties, which can be exploited to develop new exploration operators for evolutionary algorithms. We use the newly proposed operator on some well-known algorithms and show that the performance of the algorithms can be improved significantly.
The ARIEL mission main goal is the measurement of atmospheres of transiting planets. This requires the observation of two types of events: primary and secondary eclipses. In order to yield measurements of sufficient S...
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ISBN:
(纸本)9781538634622
The ARIEL mission main goal is the measurement of atmospheres of transiting planets. This requires the observation of two types of events: primary and secondary eclipses. In order to yield measurements of sufficient Signal-to-Noise Ratio to fulfill the mission objectives, the events of each exoplanet have to be observed several times. In addition, several criteria have to be considered to carry out each observation, such as the exoplanet visibility, its event duration, its potential significance in the survey, and no overlapping with other tasks. Consequently, obtaining a long term mission plan becomes unaffordable for human planners due to the complexity of computing the huge number of possible combinations for finding an optimum solution. In this contribution we present a mission planning tool based on evolutionary algorithms, which are focused on solving optimization problems such as the planning of several tasks. Specifically, the proposed tool finds a solution that highly optimizes the defined objectives, which are based on the maximization of the time spent on scientific observations and the scientific return. The results obtained on the large experimental set up support that the proposed scheduler technology is robust and can function in a variety of scenarios, offering a competitive performance which does not depend on the collection of exoplanets to be observed.
Traditional simulation-based protein design considers energy minimization of candidate conformations as a singleobjective combinatorial optimization problem. In this paper we consider a challenging protein design prob...
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ISBN:
(纸本)9781450349208
Traditional simulation-based protein design considers energy minimization of candidate conformations as a singleobjective combinatorial optimization problem. In this paper we consider a challenging protein design problem, producing twelve protein species based on collagen that uniquely assort into four groups of three: a problem defined herein as a 4-level heterotrimer. We formulate a bi-objective combinatorial minimization problem that targets both stability and specificity of the 4-level heterotrimer. In order to approximate its Pareto frontier, we utilize both evolutionary and nonevolutionary approaches, operating in either Pareto or aggregation fashions. Our practical observations suggest that the SMS-EMOA with Evolution Strategies' operators is more effective than standard heuristics deployed in computational protein design, such as Simulated Annealing, Replica Exchange or the Canonical Genetic Algorithm. We investigate the attained Pareto optimal sets using Barrier Tree analysis, aiming to provide insights into the chemical search-space, as well as to explain the observed algorithmic trends. In particular, we identify Replica Exchange as a promising non-evolutionary technique for this problem class, due to its efficient exploration capabilities. Overall, a common high-level protocol for simultaneous landscape analysis of evolutionary and nonevolutionary search methodologies is put forward for the first time.
Robotic swarms are often used for solving different tasks. Many articles are focused on generating robot controllers for swarm behaviour using evolutionary algorithms. Most of them are nevertheless considering only ho...
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Robotic swarms are often used for solving different tasks. Many articles are focused on generating robot controllers for swarm behaviour using evolutionary algorithms. Most of them are nevertheless considering only homogenous robots. The goal of this thesis is to use evolutionary algorithms for behaviours of heterogeneous robotic swarms. A 2D simulation was implemented to explore swarm controller optimization methods with the ability to create custom scenarios for robotic swarms. We tested differential evolution and evolution strategies on three different scenarios.
This paper discusses the problem of business process optimization within a multi-objective evolutionary framework. Business process optimization (BPO) is considered as the problem of constructing feasible business pro...
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ISBN:
(纸本)9781509046010
This paper discusses the problem of business process optimization within a multi-objective evolutionary framework. Business process optimization (BPO) is considered as the problem of constructing feasible business process designs with optimum attribute values such as duration and cost. The proposed approach involves a pre-processing stage and the application of a series of evolutionary Multi-Objective Optimization algorithms (EMOAs) in an attempt to generate a series of diverse optimized business process designs for the same process requirements. The proposed optimization framework introduces a quantitative representation of business processes involving two matrices one for capturing the process design and one for calculating and evaluating the process attributes. It also introduces an algorithm that checks the feasibility of each candidate solution (i.e. process design). The work presented in this paper is aimed to investigate the benefits that come from the utilization of a pre-processing stage in the execution process of the EMOAs. The experimental results demonstrate that the proposed optimization framework is capable of producing a satisfactory number of optimized design alternatives considering the problem complexity and high rate of infeasibility. The addition of the pre-processing stage appears to have a positive effect on the framework by producing more non-dominated solutions in reduced time frames.
We present a parallel evolutionary algorithm with interleaving generations. The algorithm uses a careful analysis of genetic operators and selection in order to evaluate individuals from following generations while th...
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ISBN:
(纸本)9781450349208
We present a parallel evolutionary algorithm with interleaving generations. The algorithm uses a careful analysis of genetic operators and selection in order to evaluate individuals from following generations while the current generation is still not completely evaluated. This brings significant advantages in cases where each fitness evaluation takes different amount of time, the evaluations are performed in parallel, and a traditional generational evolutionary algorithm has to wait for all evaluations to finish. The proposed algorithm provides better utilization of computational resources in these cases. Moreover, the algorithm is functionally equivalent to the generational evolutionary algorithm, and thus it does not have any evaluation time bias, which is often present in asynchronous evolutionary algorithms. The proposed algorithm is tested in a series of simple experiments and its effectiveness is compared to the effectiveness of the generational evolutionary algorithm in terms of CPU utilization.
The bin-packing problem is a widely studied combinatorial optimization problem. In classical bin-packing problem, we are given a set of real numbers in the range (0,1] and the goal is to place them in minimum number o...
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ISBN:
(纸本)9789811065446;9789811065439
The bin-packing problem is a widely studied combinatorial optimization problem. In classical bin-packing problem, we are given a set of real numbers in the range (0,1] and the goal is to place them in minimum number of bins so that no bin holds more than one. In this paper we consider a bi-dimensional bin-packing in which we are given a set of rectangular items to be packed into minimum number of fixed size of square bins. Here we consider two objectives applied on a bi-dimensional variant, one is related to minimize number of bins and second is minimize average percentage of wastage/gaps in bins. To solve this problem we incorporate the concept of Pareto's optimality to evolve set of solutions using evolutionary algorithm (EA) tool hybridized with the heuristic operator leading to improve results from existing techniques.
Human societies around the world interact with each other by developing and maintaining social norms, and it is critically important to understand how such norms emerge and change. In this work, we de fine an evolutio...
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ISBN:
(纸本)9781510855076
Human societies around the world interact with each other by developing and maintaining social norms, and it is critically important to understand how such norms emerge and change. In this work, we de fine an evolutionary gametheoretic model to study how norms change in a society, based on the idea that different strength of norms in societies translate to different game-theoretic interaction structures and incentives. We use this model to study, both analytically and with extensive agent-based simulations, the evolutionary relationships of the need for coordination in a society (which is related to its norm strength) with two key aspects of norm change: cultural inertia (whether or how quickly the population responds when faced with conditions that make a norm change desirable), and exploration rate (the willingness of agents to try out new strategies). Our results show that a high need for coordination leads to both high cultural inertia and a low exploration rate, while a low need for coordination leads to low cultural inertia and high exploration rate. This is the first work, to our knowledge, on understanding the evolutionary causal relationships among these factors.
This paper presents general finite impulse response (FIR) digital filter design with asymmetric coefficients to approximate passband and stopband magnitude responses and constant passband group delay specifications us...
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ISBN:
(纸本)9781509055388
This paper presents general finite impulse response (FIR) digital filter design with asymmetric coefficients to approximate passband and stopband magnitude responses and constant passband group delay specifications using an evolutionary optimization algorithm called the Interactive Self Learning Algorithm (ISLA). Lowpass and bandpass digital filters are chosen and their design results are shown to demonstrate the effectiveness of the approach. Results indicate that passband and stopband peak magnitude errors and passband peak group delay error can be designed to approximate given specifications.
Large service organizations such as telecom or utility companies face numerous decision management problems, many of which are related to the management of various resources. The management of spare parts and inventor...
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ISBN:
(纸本)9781509046010
Large service organizations such as telecom or utility companies face numerous decision management problems, many of which are related to the management of various resources. The management of spare parts and inventory is one of the key resource management problems an organization faces on a daily basis. Especially their timely availability can have serious impacts on the service quality and the customer satisfaction. Lack of visibility and availability of spare parts in the right place and at the right time can lead to traveling longer distance to supply the parts or in worst case a disruption of the service. We propose to automate the management of spare parts for a legacy technology in a telecom network by leveraging an evolutionary algorithm for optimizing the distribution of spare parts. Our results show that a significant gain can be made in comparison to a assignment typically performed in a manual mechanism.
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