A method for designing optimal interval type-2 fuzzy logic controllers using evolutionary algorithms is presented in this paper. Interval type-2 fuzzy controllers can outperform conventional type-1 fuzzy controllers w...
详细信息
A method for designing optimal interval type-2 fuzzy logic controllers using evolutionary algorithms is presented in this paper. Interval type-2 fuzzy controllers can outperform conventional type-1 fuzzy controllers when the problem has a high degree of uncertainty. However, designing interval type-2 fuzzy controllers is more difficult because there are more parameters involved. In this paper, interval type-2 fuzzy systems are approximated with the average of two type-1 fuzzy systems, which has been shown to give good results in control if the type-1 fuzzy systems can be obtained appropriately. An evolutionary algorithm is applied to find the optimal interval type-2 fuzzy system as mentioned above. The human evolutionary model is applied for optimizing the interval type-2 fuzzy controller for a particular non-linear plant and results are compared against an optimal type-1 fuzzy controller. A comparative study of simulation results of the type-2 and type-1 fuzzy controllers, under different noise levels, is also presented. Simulation results show that interval type-2 fuzzy controllers obtained with the evolutionary algorithm outperform type-1 fuzzy controllers.
The basic information required to utilize one of possible computation tools/algorithms (mainly the evolution strategy) to solve a wide class of real practical engineering optimization problems is presented and discuss...
详细信息
ISBN:
(纸本)9781538621653
The basic information required to utilize one of possible computation tools/algorithms (mainly the evolution strategy) to solve a wide class of real practical engineering optimization problems is presented and discussed in the present paper. The effectiveness of the considered method is demonstrated by the possibility of the use of different form of objective functions, various and numerous nonlinear constraints and different types of design variables (continuous, discrete, real, integer). The sensitivity of the algorithm to the choice of the evolution strategy parameters is also discussed herein. The generality of the evolution strategy is illustrated by the analysis of three examples dealing with: the design of helical springs, the buckling of cylindrical composite panels and the buckling of pressure vessels with domed heads.
This paper describes the design of an artificial intelligent opponent in the Empire Wars turn-based strategy computer game. Several approaches to make the opponent in the game, that has complex rules and a huge state ...
详细信息
ISBN:
(数字)9783319544724
ISBN:
(纸本)9783319544724
This paper describes the design of an artificial intelligent opponent in the Empire Wars turn-based strategy computer game. Several approaches to make the opponent in the game, that has complex rules and a huge state space, are tested. In the first phase, common methods such as heuristics, influence maps, and decision trees are used. While they have many advantages (speed, simplicity and the ability to find a solution in a reasonable time), they provide rather average results. In the second phase, the player is enhanced by an evolutionary algorithm. The algorithm adjusts several parameters of the player that were originally determined empirically. In the third phase, a learning process based on recorded moves from previous games played is used. The results show that incorporating evolutionary algorithms can significantly improve the efficiency of the artificial player without necessarily increasing the processing time.
Machine learning methods have been increasingly applied to structural brain magnetic resonance imaging (MRI) scans for predicting clinical phenotypes at the individual level. Despite significant methodological develop...
详细信息
ISBN:
(纸本)9781538621653
Machine learning methods have been increasingly applied to structural brain magnetic resonance imaging (MRI) scans for predicting clinical phenotypes at the individual level. Despite significant methodological developments, reducing the dimensionality of the features extracted from brain MRI data remains a major challenge. In this paper, we propose a genetic algorithm based feature selection approach to binary phenotype prediction using structural brain MRI. We divide the population of individuals into multiple tribes and modify the initialization and evolutionary operations to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe is able to focus on exploring a specific part of the solution space. We also incorporate tribe competition into the evolution process, which allows the tribe that produces better individuals to enlarge its sizes so as to have more individuals to search the sub solution space it explores. We have evaluated our proposed approach against eight wrapper and nine filter feature selection methods on the binary phenotype prediction dataset used in the MICCAI 2014 Machine Learning Challenge. Our results indicate that the proposed approach can identify the optimal feature subset more effectively and is able to produce more accurate binary phenotype prediction.
When geo-locating ground objects from a UAV, multiple views of the same object can lead to improved geolocation accuracy. Of equal importance to the location estimate, however, is the uncertainty estimate associated w...
详细信息
ISBN:
(数字)9781510609006
ISBN:
(纸本)9781510608993;9781510609006
When geo-locating ground objects from a UAV, multiple views of the same object can lead to improved geolocation accuracy. Of equal importance to the location estimate, however, is the uncertainty estimate associated with that location. Standard methods for estimating uncertainty from multiple views generally assume that each view represents an independent measurement of the geo-location. Unfortunately, this assumption is often violated due to correlation between the location estimates. This correlation may occur due to the measurements coming from the same platform, meaning that the error in attitude or location may be correlated across time;or it may be due to external sources (such as GPS) having the same error in multiple aircraft. In either case, the geo-location estimates are not truly independent, leading to optimistic estimates of the geo-location uncertainty. For distributed data fusion applications, correlation-agnostic fusion methods have been developed that can fuse data together regardless of how much correlation may be present between the two estimates. While the results are generally not as impressive as when correlation is perfectly known and taken into account, the fused uncertainty results are guaranteed to be conservative and an improvement on operating without fusion. In this paper, we apply a selection of these correlation-agnostic fusion techniques to the multi-view geo-location problem and analyze their effects on geo-location and predicted uncertainty accuracy. We find that significant benefits can be found from applying these correlation agnostic fusion effects, but that they vary greatly in how well they estimate their own uncertainty.
Swarm of drones are increasingly deployed to perform a variety of critical missions such as surveillance, rescue in disaster areas etc. To guarantee success of a mission, the controlling software should pursue two goa...
详细信息
ISBN:
(纸本)9781538623879
Swarm of drones are increasingly deployed to perform a variety of critical missions such as surveillance, rescue in disaster areas etc. To guarantee success of a mission, the controlling software should pursue two goals. Firstly, it should ensure safety, i.e., guarantee collision avoidance. Secondly, it should prevent a premature depletion of the batteries of the drones by minimizing their travel paths. In this paper, we propose an approach that combines run-time safety monitoring and high performance evolutionary algorithm to predict dynamically emerging hazards. High performance of the route calculation algorithm allows us to ensure that the routes of drones are dynamically adjusted to avoid collisions while maintaining efficiency. The benchmarking of the proposed approach validates its efficiency and safety.
This paper presents a novel approach to the source seeking problem, where a group of mobile agents tries to locate the maximum of a scalar field defined on the space in which they are moving. The agents know their pos...
详细信息
It is known that neighbourhood structures affect search performance. In this study we analyse a series of neighbourhood structures to facilitate the search. The well known steepest descent (SD) local search algorithm ...
详细信息
ISBN:
(纸本)9783319516912;9783319516905
It is known that neighbourhood structures affect search performance. In this study we analyse a series of neighbourhood structures to facilitate the search. The well known steepest descent (SD) local search algorithm is used in this study as it is parameter free. The search problem used is the Google Machine Reassignment Problem (GMRP). GMRP is a recent real world problem proposed at ROADEF/EURO challenge 2012 competition. It consists in reassigning a set of services into a set of machines for which the aim is to improve the machine usage while satisfying numerous constraints. In this paper, the effectiveness of three neighbourhood structures and their combinations are evaluated on GMRP instances, which are very diverse in terms of number of processes, resources and machines. The results show that neighbourhood structure does have impact on search performance. A combined neighbourhood structures with SD can achieve results better than SD with single neighbourhood structure.
This paper describes the application of a Differential Evolution based approach for inducing oblique decision trees in a global search strategy. By using both the number of attributes and the number of class labels in...
详细信息
ISBN:
(数字)9783319573519
ISBN:
(纸本)9783319573519;9783319573502
This paper describes the application of a Differential Evolution based approach for inducing oblique decision trees in a global search strategy. By using both the number of attributes and the number of class labels in a dataset, this approach determines the size of the real-valued vector utilized for encoding the set of hyperplanes used as test conditions in the internal nodes of an oblique decision tree. Also a scheme of three steps to map the linear representation of candidate solutions into feasible oblique decision trees is described. Experimental results obtained show that this approach induces more accurate classifiers than those produced by other proposed induction methods.
In the evolutionary Computation field, it is frequent to assume that a computation load necessary for fitness value computation is, at least, similar for all possible cases. The main objective of this paper is to show...
详细信息
ISBN:
(纸本)9781509046010
In the evolutionary Computation field, it is frequent to assume that a computation load necessary for fitness value computation is, at least, similar for all possible cases. The main objective of this paper is to show that the above assumption is frequently false. Therefore, the examples of evolutionary methods that use problem encoding which allows for significant optimization of the fitness computation process are pointed out and analyzed. The definition of Problem Encoding Allowing Cheap Fitness Computation of Mutated Individuals (PEACh) is proposed. Another objective of the paper is to start a discussion concerning the computation load measurement in the evolutionary computation field. As shown, the Fitness Function Evaluation number is not always a fair measure and may be significantly affected by the quality of method implementation.
暂无评论