Floating Point Genetic Algorithm Research Articles

Floating Point Genetic Algorithm Approach for Optimizing Unstructured Meshes

This paper presents a method for optimizing unstructured triangular meshes using a floating-point genetic algorithm. A mesh generation algorithm based on a modified advancing front method and sets of heuristic rules are used to generate the initial non-smooth triangular meshes for complex shapes. The developed mesh is then smoothed using a floating-point genetic algorithm that is more flexible than the usual binary genetic algorithms, and can handle non-smooth regions containing several local extrema. Three approaches are used in selecting the fitness function to be optimized in the genetic algorithm, namely, the triangle aspect ratio, the maximum angle at each node of the triangular mesh, and a weighted linear combination of both functions. The genetic algorithm has been tested and validated for a number of test cases covering a wide range of complex geometry applications. The results have shown a high degree of improvement in the quality of the smoothed meshes and an ability to handle non-convex regions.

SpiFoG: an efficient supervised learning algorithm for the network of spiking neurons

There has been a lot of research on supervised learning in spiking neural network (SNN) for a couple of decades to improve computational efficiency. However, evolutionary algorithm based supervised learning for SNN has not been investigated thoroughly which is still in embryo stage. This paper introduce an efficient algorithm (SpiFoG) to train multilayer feed forward SNN in supervised manner that uses elitist floating point genetic algorithm with hybrid crossover. The evidence from neuroscience claims that the brain uses spike times with random synaptic delays for information processing. Therefore, leaky-integrate-and-fire spiking neuron is used in this research introducing random synaptic delays. The SpiFoG allows both excitatory and inhibitory neurons by allowing a mixture of positive and negative synaptic weights. In addition, random synaptic delays are also trained with synaptic weights in an efficient manner. Moreover, computational efficiency of SpiFoG was increased by reducing the total simulation time and increasing the time step since increasing time step within the total simulation time takes less iteration. The SpiFoG is benchmarked on Iris and WBC dataset drawn from the UCI machine learning repository and found better performance than state-of-the-art techniques.

Impact of scaled fitness functions on a floating‐point genetic algorithm to optimise the operation of standalone microgrids

Standalone hybrid remote area power systems, also known as microgrids (MGs), can provide reasonably priced electricity in geographically isolated and the edge of grid locations for their operators. To achieve the reliable operation of MGs, whilst consuming minimal fossil fuels and maximising the penetration of renewables, the voltage and frequency should be maintained within acceptable limits. This can be accomplished by solving an optimisation problem. Floating-point genetic algorithm (FP-GA) is a heuristic technique that has a proven track record of effectively identifying the optimal solutions. However, in addition to needing appropriate operators, the solver needs a fitness function to yield the most optimal control variables. In this study, a suitable fitness function is formulated, by including the operational, interruption and technical costs, which are then solved with an FP-GA, with different combinations of operators. The developed fitness function and the considered operators are tested for the non-linear optimisation problem of a 38-bus MG. Detailed discussions are provided on the impact, which different operators have upon the outcomes of the fitness function.

Optimization of Matched and Mismatched Filters in Short Range Pulse Radars using Genetic Algorithm

Matched and mismatched filters are considered important parts of a radar signal processing unit. In this paper, we present an approach to optimize the matched filters and mismatched filters in short range pulse radars. For radar, the matched filter coefficients are the complex conjugates of transmitted code. We used binary phase codes as transmitted pulse. The disadvantage of binary phase codes is having high sidelobe levels in the output of correlation function. Thus, we decided to use optimization algorithms for finding binary phase codes with minimum peak sidelobe levels (MPS). After that, we succeeded in producing mismatched filter coefficients (Mis-co) for each code using floating point genetic algorithm (FGA) and we could generate and test the filter coefficients with maximum peak to sidelobe level ratio (PSR). For testing the filter, we plotted ambiguity function for each set of coefficients and tested the filter with Doppler shift.

Optimization of a Three Degrees of Freedom DELTA Manipulator for Well-Conditioned Workspace with a Floating Point Genetic Algorithm

This paper demonstrates dexterity optimization of a three degrees of freedom (3 DOF) Delta manipulator. The parallel manipulator consists of three identical chains and is able to move on all three translational axes. In order to optimize the manipulator in term of dexterity, a floating point Genetic Algorithm (GA) global search method was applied. This algorithm intends to maximize the Global Condition Index (GCI) of the manipulator over its workspace and to propose the best design parameters such as the length of the links which result in a higher GCI and thus a better dexterity.

Pattern classification using fuzzy relation and genetic algorithm

PurposeThis paper aims to consider a soft computing approach to pattern classification using the basic tools of fuzzy relational calculus (FRC) and genetic algorithm (GA).Design/methodology/approachThe paper introduces a new interpretation of multidimensional fuzzy implication (MFI) to represent the author's knowledge about the training data set. It also considers the notion of a fuzzy pattern vector (FPV) to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space. The construction of the pattern classifier is essentially based on the estimate of a fuzzy relation Ri between the antecedent clause and consequent clause of each one‐dimensional fuzzy implication. For the estimation of Ri floating point representation of GA is used. Thus, a set of fuzzy relations is formed from the new interpretation of MFI. This set of fuzzy relations is termed as the core of the pattern classifier. Once the classifier is constructed the non‐fuzzy features of a test pattern can be classified.FindingsThe performance of the proposed scheme is tested on synthetic data. Subsequently, the paper uses the proposed scheme for the vowel classification problem of an Indian language. In all these case studies the recognition score of the proposed method is very good. Finally, a benchmark of performance is established by considering Multilayer Perceptron (MLP), Support Vector Machine (SVM) and the proposed method. The Abalone, Hosse colic and Pima Indians data sets, obtained from UCL database repository are used for the said benchmark study. The benchmark study also establishes the superiority of the proposed method.Originality/valueThis new soft computing approach to pattern classification is based on a new interpretation of MFI and a novel notion of FPV. A set of fuzzy relations which is the core of the pattern classifier, is estimated using floating point GA and very effective classification of patterns under vague and imprecise environment is performed. This new approach to pattern classification avoids the curse of high dimensionality of feature vector. It can provide multiple classifications under overlapped classes.

Controlling gaze with an embodied interactive control architecture

Human-Robot Interaction (HRI) is a growing field of research that targets the development of robots which are easy to operate, more engaging and more entertaining. Natural human-like behavior is considered by many researchers as an important target of HRI. Research in Human-Human communications revealed that gaze control is one of the major interactive behaviors used by humans in close encounters. Human-like gaze control is then one of the important behaviors that a robot should have in order to provide natural interactions with human partners. To develop human-like natural gaze control that can integrate easily with other behaviors of the robot, a flexible robotic architecture is needed. Most robotic architectures available were developed with autonomous robots in mind. Although robots developed for HRI are usually autonomous, their autonomy is combined with interactivity, which adds more challenges on the design of the robotic architectures supporting them. This paper reports the development and evaluation of two gaze controllers using a new cross-platform robotic architecture for HRI applications called EICA (The Embodied Interactive Control Architecture), that was designed to meet those challenges emphasizing how low level attention focusing and action integration are implemented. Evaluation of the gaze controllers revealed human-like behavior in terms of mutual attention, gaze toward partner, and mutual gaze. The paper also reports a novel Floating Point Genetic Algorithm (FPGA) for learning the parameters of various processes of the gaze controller.

Comparative Study of Three-Dimensional Wing Drag Minimization by Different Optimization Techniques

The main goal of this paper is to document a comparative study of different computational-fluid-dynamics-based optimization techniques applied to the solution of a three-dimensional wing drag minimization problem. To achieve this objective, three optimization tools were used: SYN107 (Intelligent Aerodynamics International), MDOPT (The Boeing Company), and OPTIMAS (Israel Aerospace Industries). The first tool employs gradient-based search techniques using the continuous adjoint equation, the second one is a response-surface method, and the last one uses a floating-point genetic algorithm as its search engine. As the starting geometry, the public domain DPW-W1 wing (a test case for the Third AIAA Drag Prediction Workshop) was used. The comparisons included herein are provided in three stages: cross analysis of the initial geometry by the computational fluid dynamics tools employed in the optimizations, optimization of the initial geometry to minimum drag, and cross analysis of optimal shapes achieved by the optimization tools using all computational fluid dynamics tools employed. The cross analysis also includes results from an independent computational fluid dynamics method that was not used in any of the optimization efforts. These results help quantify the level of variation that is inherent in, and can be expected from, application of the current state-of-the-art aerodynamic optimization methods. The present work may be regarded as a move toward the construction of reliable test cases for an aerodynamic shape optimization problem. Another goal of this collaborative investigation is to collect lessons learned from this pilot project to help develop a model for an aerodynamic optimization workshop.

A novel method for real parameter optimization based on Gene Expression Programming

Gene Expression Programming (GEP) is a new technique of evolutionary algorithm that implements genome/phoneme representation in computing programs. Due to its power in global search, it is widely applied in symbolic regression. However, little work has been done to apply it to real parameter optimization yet. This paper proposes a real parameter optimization method named Uniform-Constants based GEP (UC-GEP). In UC-GEP, the constant domain directly participates in the evolution. Our research conducted extensive experiments over nine benchmark functions from the IEEE Congress on Evolutionary Computation 2005 and compared the results to three other algorithms namely Meta-Constants based GEP (MC-GEP), Meta-Uniform-Constants based GEP (MUC-GEP), and the Floating Point Genetic Algorithm (FP-GA). For simplicity, all GEP methods adopt a one-tier index gene structure. The results demonstrate the optimal performance of our UC-GEP in solving multimodal problems and show that at least one GEP method outperforms FP-GA on all test functions with higher computational complexity.

Revision of a Floating-Point Genetic Algorithm GENOCOP V for Nonlinear Programming Problems

In this paper, focusing on general nonlinear programming problems, we attempt to propose a general and high- performance approximate solution method for them. In recent years, S. Koziel et al. have proposed a floating-point ge- netic algorithm, GENOCOP V, as a general approximate solution method for nonlinear programming problems and showed its efficiency, there are left some shortcomings of the method. In this paper, incorporating ideas to cope with these shortcomings, we propose a revised GENOCOP V (RGENOCOP V). Furthermore, we show the efficiency of the pro- posed method RGENOCOP V by comparing it with two existing methods, RGENOCOP III and GENOCOP V through the application of them into the numerical examples.

A floating-point genetic algorithm for solving the unit commitment problem

This paper proposes a floating-point genetic algorithm (FPGA) to solve the unit commitment problem (UCP). Based on the characteristics of typical load demand, a floating-point chromosome representation and an encoding–decoding scheme are designed to reduce the complexities in handling the minimum up/down time limits. Strategic parameters of the FPGA are characterized in detail, i.e., the evaluation function and its constraints, population size, operation styles of selection, crossover operation and probability, mutation operation and probability. A dynamic combination scheme of genetic operators is formulated to explore and exploit the FPGA in the non-convex solution space and multimodal objective function. Experiment results show that the FPGA is a more effective technique among the various styles of genetic algorithms, which can be applied to the practical scheduling tasks in utility power systems.

Volterra-system identification using adaptive real-coded genetic algorithm

In this paper, a floating-point genetic algorithm (GA) for Volterra-system identification is presented. The adaptive GA method suggested here addresses the problem of determining the proper Volterra candidates, which leads to the smallest error between the identified nonlinear system and the Volterra model. This is achieved by using variable-length GA chromosomes, which encode the coefficients of the selected candidates. The algorithm relies on sorting all candidates according to their correlation with the output. A certain number of candidates with the highest correlation with the output are selected to undergo the first evolution During the process of evolution the candidates with the least significant contribution in the error-reduction process is removed. Then, the next set of candidates are applied into the next era. The process continues until a solution is found. The proposed GA method handles the issues of detecting the proper Volterra candidates and calculating the associated coefficients as a nonseparable process. The fitness function employed by the algorithm prevents irrelevant candidates from taking part in the final solution. Genetic operators are chosen to suit the floating-point representation of the genetic data. As the evolution process improves and the method reaches a near-global solution, a local search is implicitly applied by zooming in on the search interval of each gene by adaptively changing the boundaries of those intervals. The proposed algorithms have produced excellent results in modeling different nonlinear systems with white and colored Gaussian inputs with/without white Gaussian measurement noise

Volterra system identification using adaptive genetic algorithms

Abstract In this paper a floating point genetic algorithm (GA) for Volterra system identification is presented. The adaptive GA method suggested here addresses the problem of determining the proper Volterra candidates which leads to the smallest error between the identified nonlinear system and the Volterra model. This is achieved by using variable length GA chromosomes which encode the coefficients of the selected candidates. During the process of evolution the candidates with the least significant contribution in the error reduction process is removed. The proposed GA method detects the proper Volterra candidates and the associated coefficients in one single evolutionary process. The fitness function employed by the algorithm prevents irrelevant candidates from taking part of the final solution. Genetic operators are chosen to suit the floating point representation of the genetic data. As the evolution process improves and the method reaches a near-global solution, a local search is implicitly applied by zooming in the search interval of each gene by adaptively changing the boundaries of those intervals. The proposed algorithms has produced excellent results in modeling different nonlinear systems.

The evolution of insurance markets under adverse selection

This paper applies a floating-point genetic algorithm to an insurance model in which the principals who underwrite insurance contracts cannot observe the risk characteristics of the agents who sign those contracts. Under perfect rationality, the equilibria of this model are analogous to those in Rothschild and Stiglitz (1976). The learning version of the model exhibits interesting transition dynamics as well as novel outcomes. When the learning model converges to the equilibrium from the model with perfect rationality, pooling contracts break down over time and the set of contracts bifurcates, with one group of principals serving high-risk agents and another group serving low-risk agents. When there are relatively few low-risk agents in the market, the economy converges to contracts that provide full insurance for high-risk agents, while low-risk agents are not served at all. When there are relatively few high-risk agents in the market, some types of pooling contracts are sustainable. These last two results are at odds with the outcomes under models of perfect rationality.

DISCRETE COST OPTIMIZATION OF COMPOSITE FLOORS USING A FLOATING-POINT GENETIC ALGORITHM

The biologically-inspired genetic algorithm (GA) is usually implemented by binary representation of parameters, which is simple to create and manipulate. The binary representation, however, requires excessive computational resources when the number of variables is large or a high level of precision is required. An alternative to the popular binary genetic algorithm is a floating-point parameter GA where each variable is represented by a floating point number and linear interpolation is used to combine variable values to crossover from parents to an offspring. This article presents cost optimization of composite floors using a floating point genetic algorithm. The total cost function includes the costs of (a) concrete, (b) steel beam, and (c) shear studs. The design is based on the AISC Load and Resistance Factor Design (LRFD) specifications and plastic design concepts. Based on a comparison with example designs presented in the literature it is concluded that a formal cost optimization can result in substantial cost savings.

An interactive fuzzy satisficing method for multiobjective nonconvex programming problems through floating-point genetic algorithms

In this paper, we focus on multiobjective nonconvex nonlinear programming problems and present an interactive fuzzy satisficing method through floating point genetic algorithms. After determining the fuzzy goals of the decision maker, if the decision maker specifies the reference membership values, the corresponding Pareto optimal solution can be obtained by solving the augmented minimax problems for which the floating point genetic algorithm, called GENOCOP III, is applicable. In order to overcome the drawbacks of GENOCOP III, we propose the revised GENOCOP III by introducing a method for generating an initial feasible point and a bisection method for generating a new feasible point efficiently. Then an interactive fuzzy satisficing method for deriving a satisficing solution for the decision maker efficiently from a Pareto optimal solution set is presented together with an illustrative numerical example.

An interactive fuzzy satisficing method for multiobjective nonconvex programming problems through floating point genetic algorithms

In this paper, we focus on multiobjective nonconvex nonlinear programming problems and present an interactive fuzzy satisficing method through floating point genetic algorithms. After determining the fuzzy goals of the decision maker, if the decision maker specifies the reference membership values, the corresponding Pareto optimal solution can be obtained by solving the augmented minimax problems for which the floating point genetic algorithm, called GENOCOP III, is applicable. In order to overcome the drawbacks of GENOCOP III, we propose the revised GENOCOP III by introducing a method for generating an initial feasible point and a bisection method for generating a new feasible point efficiently. Then an interactive fuzzy satisficing method for deriving a satisficing solution for the decision maker efficiently from a Pareto optimal solution set is presented together with an illustrative numerical example.

Solving the forward kinematics of parallel manipulators with a genetic algorithm

A floating point genetic algorithm is proposed to solve the forward kinematic problem for parallel manipulators. This method, adapted from studies in the biological sciences, allows the use of inverse kinematic solutions to solve forward kinematics as an optimization problem. The method is applied to two 3-degree-of-freedom planar parallel manipulators and to a 3-degree-of-freedom spherical manipulator. The method converges to a solution within a broader search domain compared to a Newton-Raphson scheme. © 1996 John Wiley & Sons, Inc.