Multiple Fitness Functions Research Articles

Integrated ANN-LWPA for cutting parameter optimization in WEDM

In this paper, we present a new approach to determinate cutting parameters in wire electrical discharge machining (WEDM), integrated artificial neuron network (ANN), and wolf pack algorithm based on the strategy of the leader (LWPA). The cutting parameters considered in this paper are pulse-on, current, water pressure, and cutting feed rate. Models of the effects of the four parameters on machining time (Tp), machining cost (Cp), and surface roughness (Ra) are mathematically constructed. An ANN-LWPA integration system with multiple fitness functions is proposed to solve the modelling problem. By using the proposed approach, this study demonstrates that Tp, Cp, and Ra can be estimated at 164.1852 min, 239.5442 RMB, and 1.0223 μm in single objective optimization, respectively. For example, as for Ra, integrated ANN-LWPA has optimized the Ra value by the reduction of 0.1337 μm (11.6 %), 0.3377 μm (24.8 %), and 0.105 μm (10.3 %) compared to experimental data, regression model, and ANN model, respectively. Consequently, the ANN-LWPA integration system boasts some advantages over decreasing the value of fitness functions by comparison with the experimental regression model, ANN model, and conventional LWPA result. Moreover, the proposed integration system can be also utilized to obtain multiple solutions by uniform design-based exploration. Therefore, in order to solve complex machining optimization problems, an intelligent process scheme could be integrated into the numeric control system of WEDM.

Optimum portfolio selection using a hybrid genetic algorithm and analytic hierarchy process

Purpose – The purpose of this paper is to present a multi-objective model to the optimum portfolio selection using genetic algorithm and analytic hierarchy process (AHP). Portfolio selection is a multi-objective decision-making problem in financial management. Design/methodology/approach – The proposed approach solves the problem in two stages. In the first stage, the portfolio selection problem is formulated as a zero-one mathematical programming model to optimize two objectives, namely, return and risk. A genetic algorithm (GA) with multiple fitness functions called as Multiple Fitness Functions Genetic Algorithm is applied to solve the formulated model. The proposed GA results in several non-dominated portfolios being in the Pareto (efficient) frontier. A decision-making approach based on AHP is then used in the second stage to select the portfolio from among the solutions obtained by GA which satisfies a decision-maker’s interests at most. Findings – The proposed decision-making system enables an investor to find a portfolio which suits for his/her expectations at most. The main advantage of the proposed method is to provide prima-facie information about the optimal portfolios lying on the efficient frontier and thus helps investors to decide the appropriate investment alternatives. Originality/value – The value of the paper is due to its comprehensiveness in which seven criteria are taken into account in the selection of a portfolio including return, risk, beta ratio, liquidity ratio, reward to variability ratio, Treynor’s ratio and Jensen’s alpha.

A Multiple Objective Fitness Function Design Scheme for GA-Based Cognitive Engines

In this paper, we propose a multiple objective fitness function for cognitive engines by using the genetic algorithm (GA). Specifically, we propose four single objective fitness functions, and finally, we propose a multiple objective fitness function based on the single objective fitness functions for transmission parameter optimization. Numerical results demonstrate that we can obtain transmission parameter sets optimized for given transmission scenarios with the GA-based cognitive engine incorporating the proposed objective fitness function.

Multi-Objective Firefly Algorithm for Multi-Class Gene Selection

Multiclass cancer classification is an emerging technique which presents the possibility of cancer identification using microarray data. For selecting genes in the multiclass gene categorization filter methods are frequently used. But the filter method is not applicable for some of the multiclass microarray data sets because of the rigorous heterogeneity of biological tissues and samples. So, for selecting genes decay the multiclass ranking statistics into class explicit statistics and then Pareto-front analysis is used. Also, to identify the Pareto-optimal set the non-dominant sorting genetic algorithm is suggested. But the drawback is this method does not scale with high complexity. Because, where the number of elements which are represented to mutation is large there is an exponential raise in search space size. So, in this manuscript an innovative technique is introduced which is called Multiobjective Firefly Algorithm for Multiclass Gene Selection (MFGS). A firefly has a tendency to be fascinated towards other fireflies with superior flash intensity. The multiple objective firefly algorithms intend to optimize two or more conflicting characteristics represented by fitness functions. In the multiple objective firefly method, a set of Pareto-optimal solutions are created which concurrently optimize the contradictory necessities of the multiple fitness functions. In the proposed method, the genes are selected by optimizing the number of fireflies in the multiple class-specific statistics. An experimental result shows that when compared to the existing method, there is less complexity, high classification accuracy of the proposed MFGS method.

A Multi Objective Evolutionary Algorithm for Solving a Real Health Care Fleet Optimization Problem

The problem of the transportation of patients from or to some health care center given a number of vehicles of different kinds can be considered as a common Vehicle Routing Problem (VPR). However, in our particular case, the logistics behind the generation of the vehicle itineraries are affected by a high number of requirements and constraints such as the enterprise benefits, the satisfaction of the patients, and the respect of certain law regulations regarding the patients and the employees. In this work, we discuss the main aspects of the implementation of a Multi Objective Evolutionary Algorithm focused on providing a set of valid solutions to the end users of Patient Transport Services. We provide a detailed description of the process of integrating all the information on different genetic operators and multiple fitness functions. Finally, we present the preliminary results on a real-life problem from an small company that provides transport service and we compare the results that our implementation gets with the itineraries proposed by human experts.

동적 스펙트럼 접근을 위한 유전자 알고리즘 기반 전송 매개변수 최적화 기법

본 논문에서는 동적 스펙트럼 접근을 위한 유전자 알고리즘 기반 전송 매개변수 최적화 기법을 제안한다. 구체적으로는 전송 매개변수 최적화를 위해 다목적 적합도 함수를 단일 목적 적합도 함수들의 가중합으로 표현하고, 유전자 알고리즘을 이용하여 주어진 전송 시나리오에 최적화된 전송 매개변수 값을 얻는다. 모의실험을 통하여 제안한 다목적 적합도 함수를 이용하여 주어진 시나리오에 따라 전송 매개변수를 최적화한 결과를 보인다. In this paper, we propose a transmission parameter optimization scheme based on genetic algorithm for dynamic spectrum access systems. Specifically, we represent a multiple objective fitness function as a weighted sum of single objective fitness functions to optimize transmission parameters, and then, obtain optimized transmission parameters based on genetic algorithm for given transmission scenarios. From numerical results, we confirm that the transmission parameters are well optimized by using the proposed optimization scheme.

Individual differences in leech heart motor neuron models

Motor neurons are frequently overlooked as critical contributors to the programming of motor output except where they directly play a role in central pattern generators (CPGs). Leech heart motor neurons have been shown to contribute significant phase shifts to the rhythmic motor patterns they produce [1-3], although the most important factor in pattern formation is the interaction of CPG output and synaptic weights from the CPG onto the motor neurons [3]. We seek to address the question of which neural parameters, in particular active conductances, are important for functional pattern formation, and how they influence it. We are well positioned to address this question, as we have a unique dataset comprising the complete leech heart motor neuron input pattern, output pattern, and synaptic weights for multiple ganglia in many individual animals. We have begun to exploit these data to develop more realistic Hodgkin-Huxley style biophysical models of the leech heart motor neurons by constraining models optimized by a multi-objective evolutionary algorithm to fit this animal-specific data. In particular, we extended the single compartment Garcia model [1] to a multi-compartmental model and added a slow calcium and a calcium sensitive potassium channel, currents known to be present in the living motor neurons. Our model’s dimensions are derived from reconstructions of fluorophore filled leech heart motor neurons in segments 8-12. Membrane currents were distributed according to experimental data and the result of hand tuning; e.g., the fast sodium current is only present in the axonal compartment. This base model was then parameterized such that the maximal conductances of the active currents present in each compartment, as well as the electrical coupling conductance between each pair of motor neurons, were allowed to vary as free parameters in our evolutionary algorithm. We then utilized our input-output data by delivering a particular input pattern (specific animal and motor neuron pair) to a model motor neuron and then comparing the resulting output with that recorded in the same animal. In this manner, we constrained our definition of an ‘acceptable’ model by the input/output pattern recorded in individual animals – such a model must transform the input into the correct output within some reasonable error bound. To generate acceptable models we use a Multi Objective Evolutionary Algorithm (MOEA) to create a population of models from which a subset achieves ‘acceptable’ status. In this framework, multiple independent fitness functions are evaluated separately – the phase, duty cycle, spike height, slow-wave height, and within-burst inter-spike interval (ISI) for the two phases produced. The targets for these parameters are drawn from the same individual animal as the input/output data, and error bounds are based on the within-animal variability, although slightly widened for parameters such as ISI, slow-wave and spike height, as they vary greatly between animals (derived primarily from within-animal experimental variability). Acceptable model simulation traces were all but indistinguishable from intracellular recordings from the living system, with the exception of measurement noise, even though they are constrained by a small number of functional parameters. In order to identify key parameters and relationships between synaptic weights and membrane conductances which are constrained by the functional requirements of the system, we will conduct a sensitivity analysis of the acceptable models and inspect the dimensional stacking [4] of acceptable and failed models. The identified conductances can then be perturbed in the living system either via dynamic clamp or pharmacological manipulation to validate the modeling results.

The behavior of MENA oil and non-oil producing countries in international portfolio optimization

It is well documented in developed economies that portfolio investment across national borders brings benefits of increasing returns and/or reducing risk. Dividing MENA stock markets into two main groups (oil producing and non-oil producing countries), this study examines the potential role of each group in providing diversification benefits for international investors. In addition, the behavior of the long and the short-run Efficient Frontiers (EFs) constructed by each of the sub-groups and the combined MENA markets is explored. Multi-objective international portfolio models are proposed under Mean-Variance and Mean-Lower Partial Moment frameworks, and the Multiple Fitness Function Genetic Algorithm (MFFGA) is used to find the EFs of optimal portfolios. The findings indicate that the stock markets of oil producing countries can be considered as a potential avenue for international portfolio diversification for investors not only from the same countries but also from the other MENA markets. It was also found that international portfolios constructed from the combination of MENA equity markets are more stable compared to the portfolios of sub-group markets. Further, the findings indicate that the behavior of short-term EFs in the MENA region cannot be predicted by the behavior of long-term EFs.

Customizable FPGA IP Core Implementation of a General-Purpose Genetic Algorithm Engine

Hardware implementation of genetic algorithms (GAs) is gaining importance because of their proven effectiveness as optimization engines for real-time applications (e.g., evolvable hardware). Earlier hardware implementations suffer from major drawbacks such as absence of GA parameter programmability, rigid predefined system architecture, and lack of support for multiple fitness functions. In this paper, we report the design of an IP core that implements a general-purpose GA engine that addresses these problems. Specifically, the proposed GA IP core can be customized in terms of the population size, number of generations, crossover and mutation rates, random number generator seed, and the fitness function. It has been successfully synthesized and verified on a Xilinx Virtex II Pro Field programmable gate arrays device (xc2vp30-7ff896) with only 13% logic slice utilization, 1% block memory utilization for GA memory, and a clock speed of 50 MHz. The GA core has been used as a search engine for real-time adaptive healing but can be tailored to any given application by interfacing with the appropriate application-specific fitness evaluation module as well as the required storage memory and by programming the values of the desired GA parameters. The core is soft in nature i.e., a gate-level netlist is provided which can be readily integrated with the user's system. The performance of the GA core was tested using standard optimization test functions. In the hardware experiments, the proposed core either found the globally optimum solution or found a solution that was within 3.7% of the value of the globally optimal solution. The experimental test setup including the GA core achieved a speedup of around 5.16× over an analogous software implementation.

Optimisation of cutting parameters using a multi-objective genetic algorithm

This paper presents a new optimisation technique based on genetic algorithms (GA) for determination of cutting parameters in machining operations. The cutting parameters considered in this study are cutting speed, feed rate and cutting depth. The effect of these parameters on production time, production cost and roughness is mathematically formulated. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. Experimental results show that the proposed algorithm is both effective and efficient, and can be integrated into an intelligent process planning system for solving complex machining optimisation problems.

A Multi-Objective Evolutionary Algorithm Using Min-Max Strategy And Sphere Coordinate Transformation

Abstract Multi-objective evolutionary algorithms using the weighted sum of the objectives as the fitness functions feature simple execution and effectiveness in multi-objective optimization. However, they cannot fmd the Pareto solutions on the non-convex part of the Pareto frontier, and thus aze difficult to find evenly distributed solutions. Under the circumstances, this paper proposes anew evolutionary algorithm using multiple fitness functions. Although the weights generated via the sphere coordinate transformation and uniform design are used to define the fitness, the fitness is not defined by the weighted sum of the objectives. Instead, it is defined by the maximum value of the weighted normalized objectives using amin-max strategy. In this manner, the proposed algorithm can overcome the drawbacks of the algorithms using the weighted sum of the objectives, and explore the objective space to fmd approximate uniformly distributed solutions on the Pareto front gradually. The numerical simulations show t...

Alignment using genetic programming with causal trees for identification of protein functions

A hybrid evolutionary model is used to propose a hierarchical homology of protein sequences to identify protein functions systematically. The proposed model offers considerable potentials, considering the inconsistency of existing methods for predicting novel proteins. Because some novel proteins might align without meaningful conserved domains, maximizing the score of sequence alignment is not the best criterion for predicting protein functions. This work presents a decision model that can minimize the cost of making a decision for predicting protein functions using the hierarchical homologies. Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. This work describes the comparisons of nucleocapsid proteins from the putative polyprotein SARS virus and other coronaviruses in other hosts using the model.

A multi-objective genetic algorithm approach to the design of cellular manufacturing systems

In this paper, a multi-objective integer programming model is constructed for the design of cellular manufacturing systems with independent cells. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. There are some features that make the proposed algorithm different from other algorithms used in the design of cellular manufacturing systems. These include: (1) a systematic uniform design-based technique, used to determine the search directions, and (2) searching the solution space in multiple directions instead of single direction. Four problems are selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method in designing the manufacturing cells.

Behavior generation for a mobile robot based on the adaptive fitness function

We have to prepare the evaluation (fitness) function to evaluate the performance of the robot when we apply the machine learning techniques to the robot application. In many cases, the fitness function is composed of several aspects. Simple implementation to cope with the multiple fitness functions is a weighted summation. This paper presents an adaptive fitness function for the evolutionary computation to obtain the purposive behaviors through changing the weights for the fitness function. As an example task, a basic behavior in a simplified soccer game (shooting a ball into the opponent goal) is selected to show the validity of the adaptive fitness function. Simulation results and real experiments are shown, and a discussion is given.

Multiobjective programming using uniform design and genetic algorithm

The notion of Pareto-optimality is one of the major approaches to multiobjective programming. While it is desirable to find more Pareto-optimal solutions, it is also desirable to find the ones scattered uniformly over the Pareto frontier in order to provide a variety of compromise solutions to the decision maker. We design a genetic algorithm for this purpose. We compose multiple fitness functions to guide the search, where each fitness function is equal to a weighted sum of the normalized objective functions and we apply an experimental design method called uniform design to select the weights. As a result, the search directions guided by these fitness functions are scattered uniformly toward the Pareto frontier in the objective space. With multiple fitness functions, we design a selection scheme to maintain a good and diverse population. In addition, we apply the uniform design to generate a good initial population and design a new crossover operator for searching the Pareto-optimal solutions. The numerical results demonstrate that the proposed algorithm can find the Pareto-optimal solutions scattered uniformly over the Pareto frontier.