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This paper shows a successful application of evolutionary algorithms for the design and optimisation of complex real world digital circuit that is a 32-Step Traffic Lights Controller. It discusses two important features of electronic design through evolutionary processes; creativity and innovation. Results are compared to conventional design topologies; and attempt to analyse the evolved designs is...
Memetic algorithms are effective algorithms to obtain reliable and accurate solutions for complex continuous optimization problems. Nowadays, high dimensional optimization problems are an interesting field of research. The high dimensionality introduces new problems for the optimization process, requiring more scalable algorithms that, at the same time, could explore better the higher domain space...
A new stochastic optimization algorithm referred to by the authors as the `Mean-Variance Optimization' (MVO) algorithm is presented in this paper. MVO falls into the category of the so-called “population-based stochastic optimization technique.” The uniqueness of the MVO algorithm is based on the strategic transformation used for mutating the offspring based on mean-variance of the n-best dynamic...
Fuzzy regression trees are a generalization of the standard artificial intelligence technique of regression trees. The Elgasir algorithm has previously been used to create fuzzy regression trees in order to improve the performance of crisp regression trees. A weakness of this approach was that no optimisation of tree node membership functions took place. Artificial Immune Systems are an evolutionary...
In this paper, a new evolutionary algorithm referred to as importance search algorithm (ISA) is designed to solve global numerical optimization problems with continuous variables. The proposed algorithm mainly consists of initialization process and iteration process in which the initialization process is used to initialize a population of random feasible solutions, and the iteration process is accomplished...
Independent component analysis with reference (ICA-R), is a technique to incorporate prior information about the desired sources as reference signals into the contrast function of ICA so as to form an augmented Lagrangian function under the framework of constrained ICA (cICA). The ICA-R algorithm is constructed by solving the optimization problem via Newton-like learning style. Unfortunately, this...
In this paper we present self-adaptive differential evolution algorithm jDElsgo on large scale global optimization. The experimental results obtained by our algorithm on benchmark functions provided for the CEC 2010 competition and special session on Large Scale Global Optimization are presented. The experiments were performed on 20 benchmark functions with high dimension D = 1000. Obtained results...
The following topics are dealt with: ant colony optimization; molecular and quantum computing; artificial life; particle swarm intelligence; bioinformatics and bioengineering; representation and operators; coevolution and collective behavior; artificial immune systems; combinatorial and numerical optimization; autonomous mental and behavior development; constraint and uncertainty handling; cognitive...
Over the last years, interest in hybrid meta-heuristics has risen considerably in the field of optimization. Combinations of methods such as evolutionary algorithms and local searches have provided very powerful search algorithms. However, due to their complexity, the computational time of the solution search exploration remains exorbitant when large problem instances are to be solved. Therefore,...
This paper proposes an alternative approach to efficient solving of nonlinear constrained optimization problems using evolutionary algorithms. It is assumed that the separate-ness of the feasible regions, which imposes big difficulties for evolutionary search, is partially resulted from the complexity of the nonlinear constraint functions. Based on this hypothesis, an approximate model is built for...
The process of designing an evolutionary algorithm requires the definition of an adequate representation, a set of components as operators, parameters and parameters values. In practice, some operators can not be helping the evolutionary algorithm to perform his work, thus we require to be able to detect these situations. In this paper we are interested on analyzing the capabilities of off-line calibration...
This paper discusses global optimisation from a business perspective in the context of the supply chain operations. A two-silo supply chain was built for experimentation and two approaches were used for global optimisation: a classical evolutionary approach and a cooperative coevolutionary approach. The latter approach produced higher quality solutions due to its use of communication between silos...
While data clustering has a long history and a large amount of research has been devoted to the development of clustering algorithms, significant challenges still remain. One of the most important challenges in the field is dealing with high dimensional datasets. The class of clustering algorithms that utilises information from Principal Component Analysis has proven very successful in such datasets...
In this paper, the results for the CEC 2010 Competition and Special Session on Constrained Real-Parameter Optimization using the multiobjective differential evolution algorithm with spherical pruning (sp-MODE) are presented. According to the obtained results, the sp-MODE shows to be able to find feasible solutions in highly constrained search spaces.
The need for input parameter optimisation in environmental modelling is a long-known and very time-consuming task. However, to avoid tragedy, disaster propagation predictions have to satisfy hard real-time constraints. Especially small disaster control centres with limited computing resources require fast and efficient calibration methods to deliver reliable predictions in time. The combination of...
The optimization of the number and the alignment of sensors is quite important task for designing intelligent agents/robotics. Even though we could use excellent learning algorithms, it will not work well if the alignment of sensors is wrong or the number of sensors is not enough. In addition, if a large number of sensors are available, it will cause the delay of learning. In this paper, we propose...
In this paper, a multi-objective DE algorithm based on escalating strategy will be proposed. The main idea of this escalating strategy is to re-generate the whole evolutionary population with some technology, which results in a new population significantly indifferent from the old one while inheriting the evolutionary information from the history. By this way, the performance on global convergence...
The self-identification of a redundantly actuated parallel manipulator is transformed into an optimization problem, and then differential evolution algorithm is used to obtain a globally optimal solution of the kinematic parameters. Based on the kinematic equations of the parallel manipulator, a new optimization function is formulated by eliminating the passive joint angles and decoupling the kinematic...
This paper presents an approach to search robust optimal solutions with the concept of degree of robustness in multi-objective optimization problems. The definition of the degree of robustness is used in the calculation of the mean effective objective function(feff(X)) and in the evolutionary process, degree of robustness is considered as a factor to select robust solutions which can meet the demand...
A improved multi-objective evolutionary algorithm based on Three-way radix quicksort (TQIEA ) is presented in this paper for multi-objective optimization problems(MOPs). This algorithm uses the idea of three-way radix quicksort to divided the population into three sections, Recursively sort the different sections until all the individuals have been classified and assigned the fitness value. The proposed...
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