626 research outputs found
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Incremental evolution strategy for function optimization
This paper presents a novel evolutionary approach for function optimization Incremental Evolution Strategy (IES). Two strategies are proposed. One is to evolve the input variables incrementally. The whole evolution consists of several phases and one more variable is focused in each phase. The number of phases is equal to the number of variables in maximum. Each phase is composed of two stages: in the single-variable evolution (SVE) stage, evolution is taken on one independent variable in a series of cutting planes; in the multi-variable evolving (MVE) stage, the initial population is formed by integrating the populations obtained by the SVE and the MVE in the last phase. And the evolution is taken on the incremented variable set. The other strategy is a hybrid of particle swarm optimization (PSO) and evolution strategy (ES). PSO is applied to adjust the cutting planes/hyper-planes (in SVEs/MVEs) while (1+1)-ES is applied to searching optima in the cutting planes/hyper-planes. The results of experiments show that the performance of IES is generally better than that of three other evolutionary algorithms, improved normal GA, PSO and SADE_CERAF, in the sense that IES finds solutions closer to the true optima and with more optimal objective values
A sequence based genetic algorithm with local search for the travelling salesman problem
The standard Genetic Algorithm often suffers from slow convergence for solving combinatorial optimization problems. In this study, we present a sequence based genetic algorithm (SBGA) for the symmetric travelling salesman problem (TSP). In our proposed method, a set of sequences are extracted from the best individuals, which are used to guide the search of SBGA. Additionally, some procedures are applied to maintain the diversity by breaking the selected sequences into sub tours if the best individual of the population does not improve. SBGA is compared with the inver-over operator, a state-of-the-art algorithm for the TSP, on a set of benchmark TSPs. Experimental results show that the convergence speed of SBGA is very promising and much faster than that of the inver-over algorithm and that SBGA achieves a similar solution quality on all test TSPs
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A niching memetic algorithm for simultaneous clustering and feature selection
Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data
Automated offspring sizing in evolutionary algorithms
Evolutionary Algorithms (EAs) are a class of algorithms inspired by biological evolution. EAs are applicable to a wide range of problems; however, there are a number of parameters to set in order to use an EA. The performance of an EA is extremely sensitive to these parameter values; setting these parameters often requires expert knowledge of EAs. This prevents EAs from being more widely adopted by nonexperts. Parameter control, the automation of dynamic parameter value selection, has the potential to not only alleviate the burden of parameter tuning, but also to improve performance of EAs on a variety of problem classes in comparison to employing fixed parameter values. The science of parameter control in EAs is, however, still in its infancy and most published work in this area has concentrated on just a subset of the standard parameters. In particular, the control of offspring size has so far received very little attention, despite its importance for balancing exploration and exploitation. This thesis introduces three novel methods for controlling offspring size: Self- Adaptive Offspring Sizing (SAOS), Futility-Based Offspring Sizing (FuBOS), and Diversity-Guided Futility-Based Offspring Sizing (DiGFuBOS). EAs employing these methods are compared to each other and a highly tuned, fixed offspring size EA on a wide range of test problems. It is shown that an EA employing FuBOS or DiGFuBOS performs on par with the highly tuned, fixed offspring size EA on many complex problem instances, while being far more efficient in terms of fitness evaluations. Furthermore, DiGFuBOS does not introduce any new user parameters, thus truly alleviating the burden of tuning the offspring size parameter in EAs --Abstract, page iii
Genetic Algorithm for Grammar Induction and Rules Verification through a PDA Simulator
The focus of this paper is towards developing a grammatical inference system uses a genetic algorithm (GA), has a powerful global exploration capability that can exploit the optimum offspring. The implemented system runs in two phases: first, generation of grammar rules and verification and then applies the GA’s operation to optimize the rules. A pushdown automata simulator has been developed, which parse the training data over the grammar’s rules. An inverted mutation with random mask and then ‘XOR’ operator has been applied introduces diversity in the population, helps the GA not to get trapped at local optimum. Taguchi method has been incorporated to tune the parameters makes the proposed approach more robust, statistically sound and quickly convergent. The performance of the proposed system has been compared with: classical GA, random offspring GA and crowding algorithms. Overall, a grammatical inference system has been developed that employs a PDA simulator for verification
Self-Adaptation Mechanism to Control the Diversity of the Population in Genetic Algorithm
One of the problems in applying Genetic Algorithm is that there is some
situation where the evolutionary process converges too fast to a solution which
causes it to be trapped in local optima. To overcome this problem, a proper
diversity in the candidate solutions must be determined. Most existing
diversity-maintenance mechanisms require a problem specific knowledge to setup
parameters properly. This work proposes a method to control diversity of the
population without explicit parameter setting. A self-adaptation mechanism is
proposed based on the competition of preference characteristic in mating. It
can adapt the population toward proper diversity for the problems. The
experiments are carried out to measure the effectiveness of the proposed method
based on nine well-known test problems. The performance of the adaptive method
is comparable to traditional Genetic Algorithm with the best parameter setting.Comment: 17 pages, 12 figure
Reducing the Computational Effort Associated with Evolutionary Optimisation in Single Component Design
The dissertation presents innovative Evolutionary Search (ES) methods for the reduction in
computational expense associated with the optimisation of highly dimensional design
spaces. The objective is to develop a semi-automated system which successfully negotiates
complex search spaces. Such a system would be highly desirable to a human designer by
providing optimised design solutions in realistic time.
The design domain represents a real-world industrial problem concerning the optimal
material distribution on the underside of a flat roof tile with varying load and support
conditions. The designs utilise a large number of design variables (circa 400). Due to the
high computational expense associated with analysis such as finite element for detailed
evaluation, in order to produce "good" design solutions within an acceptable period of
time, the number of calls to the evaluation model must be kept to a minimum. The
objective therefore is to minimise the number of calls required to the analysis tool whilst
also achieving an optimal design solution.
To minimise the number of model evaluations for detailed shape optimisation several
evolutionary algorithms are investigated. The better performing algorithms are combined
with multi-level search techniques which have been developed to further reduce the
number of evaluations and improve quality of design solutions. Multi-level techniques
utilise a number of levels of design representation. The solutions of the coarse
representations are injected into the more detailed designs for fine grained refinement. The
techniques developed include Dynamic Shape Refinement (DSR), Modified Injection
Island Genetic Algorithm (MiiGA) and Dynamic Injection Island Genetic Algorithm
(DiiGA). The multi-level techniques are able to handle large numbers of design variables
(i.e. > 100). Based on the performance characteristics of the individual algorithms and
multi-level search techniques, distributed search techniques are proposed. These techniques
utilise different evolutionary strategies in a multi-level environment and were developed as
a way of further reducing computational expense and improve design solutions.
The results indicate a considerable potential for a significant reduction in the number of
evaluation calls during evolutionary search. In general this allows a more efficient
integration with computationally intensive analytical techniques during detailed design and
contribute significantly to those preliminary stages of the design process where a greater
degree of analysis is required to validate results from more simplistic preliminary design
models
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