Evolutionary Design Method Research Articles

Approach of non-probabilistic reliability topology optimization using evidence theory

It is of great importance to consider uncertainty due to insufficient data or imprecise information in topology optimization design. In this paper, evidence theory is presented to handle the imprecise data situation. The plausibility measure based on evidence theory is introduced to overcome the difficulty of constructing the precise probabilistic constraint. Furthermore, the topology problem in this evidence-based optimization design is solved by combined strategy of the differential evolution and superior topology technique. In order to overcome the difficulty of intensive computational cost in calculating plausibility measure, an improved method of evolutionary optimization design integrating with imprecise extremum idea is proposed. Two truss examples are given to demonstrate the proposed approach. The research indicates that deterministic results may be the failure solutions under epistemic uncertainty. Although evidence-based optimum topology designs are more conservative than deterministic results in aspects of weight and optimal structural topology layout, it gains a more robust design under epistemic uncertainty.

Role of circuit representation in evolutionary design of energy‐efficient approximate circuits

Circuit approximation has been introduced in recent years as a viable method for constructing energy-efficient electronic systems. An open problem is how to effectively obtain approximate circuits showing good compromises between key circuit parameters - the error, power consumption, area and delay. The use of evolutionary algorithms in the task of circuit approximation has led to promising results. Unfortunately, only relatively small circuit instances have been tackled because of the scalability problems of the evolutionary design method. This study demonstrates how to push the limits of the evolutionary design by choosing a more suitable representation on the one hand and a more efficient fitness function on the other hand. In particular, the authors show that employing full adders as building blocks leads to more efficient approximate circuits. The authors focused on the approximation of key arithmetic circuits such as adders and multipliers. While the evolutionary design of adders represents a rather easy benchmark problem, the design of multipliers is known to be one of the hardest problems. The authors evolved a comprehensive library of energy-efficient 12-bit multipliers with a guaranteed worst-case error. The library consists of 65 Pareto dominant solutions considering power, delay, area and error as design objectives.

Evolutionary design of nonuniform cellular structures with optimized Poisson's ratio distribution

For negative Poisson's ratio (NPR) cellular structures, most previous research focus on the design of unit cells, and then repeat the unit cell to construct uniform cellular structures. However, there is a disadvantage that these structures do not have much design freedom to achieve high-level functions, such as performing a desired deformation. As a solution, an evolutionary design method is proposed to develop nonuniform cellular structures. To conduct this method, the design domain is divided into finite unit cells with tunable Poisson's ratio (PR). With a given objective deformation, the value of each unit cell's PR is optimized using evolutionary algorithm (EA). In order to reduce the computational cost of the algorithm, discrete cosine transform (DCT) is applied to encode the structure for evolving. Considering the geometrical complexity of the optimized nonuniform cellular structures, additive manufacturing (AM) is chosen to build them physically. Both two-dimensional (2D) and three-dimensional (3D) design cases were developed and analyzed to validate the proposed method. The computational and experimental results showed good conformation with each other. Most importantly, this novel design method brings huge potential to NPR cellular structures with high-level functions and much wider applications.

Profiles evolutionary design and evaluation of mixing performance of screw elements for intermeshing counter-rotating twin-screw kneader

The profile designs of screw elements are very crucial to improving the mixing performance for the intermeshing counter-rotating twin-screw kneader. In order to find the inherent law among different profiles of screw elements and to derive a mathematical model which evolves different types of end cross-section profiles of screw elements, a universal mathematical model of end cross-section profiles of screw elements is presented in this work. Different types of profiles of screw elements, including those traditionally used can be obtained by evolutionary design method after changing the tooth number of female and male elements and other design parameters. Several typical screw elements with different types of end cross-section profiles are selected to analyze the mixing performance. Spatial flow patterns in flow channel of the screw elements are presented with particle tracking analysis by using mesh superposition technology. The mixing performance of different screw elements including distributive and dispersive mixing, contrastive analysis for some parameters, such as residence time distribution density function, segregation scale, rate of stretching, rate of shear, etc. are proposed. The results in this paper provide a data basis for the profiles selection of screw element for intermeshing counter-rotating twin-screw kneader.

Evolutionary design of complex digital circuits based on hierarchical module composition and predominant component prevention

The critical challenge in designing complex digital circuits automatically using an evolutionary algorithm is to overcome the scalability issue caused by combinatorial explosion. Because of this problem, the existing evolutionary design methods can deal with digital circuits composed of only a small number of bits. To resolve this problem, a novel evolutionary design method is proposed on the basis of hierarchical module structures and predominant component prevention. From extensive experiments, it is found that this method can automatically design digital circuits composed of a large number of bits within a polynomial time.

An extended evolutionary design method for the optimization of hydrogen networks with pressure constraint

In this work, the evolutionary method is extended to the work utility consumption and the capital cost of compressors incorporated with fresh hydrogen consumption in the optimization of hydrogen networks. The total cost matrix (TC matrix) is constructed with the cost of each possible match calculated. Further, the minimum total cost rule is proposed to compare different sources that can match the same sink. Based on this, the potential match matrix (P matrix) and the optimal match matrix (O matrix) can be determined sequentially. The fresh hydrogen, work utility consumption, total cost, and corresponding hydrogen distribution network can be obtained from the O matrix. The hydrogen network of a real refinery is studied to demonstrate the application of the extended evolutionary method. The results show that total cost determined by the extended method is 3.32 % less than that identified by the original evolutionary method, and the same as that calculated by the mathematical programming method.

Hardware-in-the-loop Simulation based Evolutionary Design of Image Filter

An evolutionary design method of image filter based on hardware-in-the-loop (HIL) simulation is proposed. An image filter evolvable system based on System Generator was designed. Experimental results and analyses demonstrate that the optimal evolved filter outperforms traditional filters both on performance and implementation costs. In comparison with the existing evolutionary design methods of image filter, the proposed strategy brings higher computational ability and more flexibility. Evolutionary algorithm of the evolvable system can be synthesized into hardware which provided a basis for the follow-up research to achieve online adaptive filtering. DOI: http://dx.doi.org/10.5755/j01.eee.20.7.3375

In silico evolution of guiding track designs for molecular shuttles powered by kinesin motors

Molecular shuttles powered by kinesin motors require guiding tracks to perform specific tasks in nanoscale devices. Here, using our simulation of molecular shuttle movements, we describe an in silico evolutionary design method that makes it possible to automatically design the guiding tracks in accordance with their functions defined by designers. With this design method, we designed two types of pre-existing guiding track modules with improved performances, as well as one with a novel function.

Multi-Objective Evolutionary Design of Adenosine Receptor Ligands

A novel multiobjective evolutionary algorithm (MOEA) for de novo design was developed and applied to the discovery of new adenosine receptor antagonists. This method consists of several iterative cycles of structure generation, evaluation, and selection. We applied an evolutionary algorithm (the so-called Molecule Commander) to generate candidate A1 adenosine receptor antagonists, which were evaluated against multiple criteria and objectives consisting of high (predicted) affinity and selectivity for the receptor, together with good ADMET properties. A pharmacophore model for the human A1 adenosine receptor (hA1AR) was created to serve as an objective function for evolution. In addition, three support vector machine models based on molecular fingerprints were developed for the other adenosine receptor subtypes (hA2A, hA2B, and hA3) and applied as negative objective functions, to aim for selectivity. Structures with a higher evolutionary fitness with respect to ADMET and pharmacophore matching scores were selected as input for the next generation and thus developed toward overall fitter ("better") compounds. We finally obtained a collection of 3946 unique compounds from which we derived chemical scaffolds. As a proof-of-principle, six of these templates were selected for actual synthesis and subsequently tested for activity toward all adenosine receptors subtypes. Interestingly, scaffolds 2 and 3 displayed low micromolar affinity for many of the adenosine receptor subtypes. To further investigate our evolutionary design method, we performed systematic modifications on scaffold 3. These modifications were guided by the substitution patterns as observed in the set of generated compounds that contained scaffold 3. We found that an increased affinity with appreciable selectivity for hA1AR over the other adenosine receptor subtypes was achieved through substitution of the scaffold; compound 3a had a Ki value of 280 nM with approximately 10-fold selectivity with respect to hA2AR, while 3g had a 1.6 μM affinity for hA1AR with negligible affinity for the hA2A, hA2B, and hA3 receptor subtypes.

Optimized Asynchronous Circuit Design based on Evolutionary Algorithm

Remarkable advantages of asynchronous circuits in comparison with their synchronous counterparts results in vast effort in designing such circuits. This paper proposes optimized asynchronous circuit design approach by exploiting potent evolutionary circuit design method. The evolutionary algorithm applies fast and accurate hazard detection technique as a fitness function. Outcomes of proposed method in designing fundamental mode asynchronous circuit in comparison with previous methodologies reveal its notable advantages like, multi level circuit design with lower number of gates which results in lower area, lower power consumption and lower cost. Experimental result demonstrate that the proposed method reduces number of gates about 16.81%. General Terms Computer aided design, Asynchronous circuit, Hazard free design.

A Chromosome Representation Encoding Intersection Points for Evolutionary Design of Fuzzy Classifiers

ABSTRACT—Unlike the conventional chromosome representation to search the shape of fuzzy membership functions, a novel encoding scheme to search the optimal intersection points between adjacent fuzzy membership functions is originally presented for evolutionary design of fuzzy classifiers. Since the proposed representation contains the intersection points directly related to the boundary of classification, it is intuitively expected that redundancy of the search space is reduced and the performance is better in comparison with the conventional encoding scheme. The experimental results show that the proposed encoding scheme gives superior or competitive performance in two real-world datasets and gives more interpretable fuzzy classifiers. This short paper has provided additional explanation to the previous works introduced in the latest conference. Key Words: Fuzzy classifiers, genetic algorithms, encoding scheme, intersection points of membership functions 1. INTRODUCTION In classical data-driven fuzzy modeling, design of optimal fuzzy classifier has been commonly dealt with as a search problem [1][2]. Optimal design of fuzzy classifiers (FCs) is one of the most complex search problems since there are various feasible solutions according to the combination of fuzzy rules and membership functions (MFs). In other words, the optimal design of FCs is described as a non-convex and multimodal search problem. Besides, the high-order and large scale problems caused by the large number of rules, MFs, and input attributes also exist in the optimal design of FCs. In order to overcome these difficulties, many researchers have applied meta-heuristic search methods to optimally design FCs. Among them, evolutionary algorithms (EAs) such as genetic algorithms (GAs) have been widely used in the much of the literature due to their search ability and reliability [1]-[3]. EAs are basically general-purpose population-based stochastic search algorithms which use multiple candidate solutions simultaneously and find the best solution [4]. Irrespective of the given problem, the first step in applying EAs is to encode solutions for the given problem into a set of parameters: a chromosome. Thus, to optimize fuzzy models in a framework of EAs, the first step is to represent a fuzzy model into a chromosome. For evolutionary design of FCs, an encoding scheme relevant to the given problem is also required because an encoding scheme plays an important role to decide the property of search space [1][4]. The conventional encoding methods for evolutionary design of FCs are mainly designed to find the shape of the fuzzy MFs as a center and width of a Gaussian MF and 3 or 4 edge points of a triangular or trapezoidal MF. However, recent related works [5], providing an intuitive insight for properties of fuzzy rules, argue that the boundary of classification is formed in the intersection points between two adjacent MFs. In other Downloaded by [Iowa State University] at 12:35 10 January 2014

Solving the Balance Problem of On-Line Role-Playing Games Using Evolutionary Algorithms

In on-line role-playing games (RPG), each race holds some attributes and skills. Each skill contains several abilities such as physical damage, hit rate, etc. Parts of the attributes and all the abilities are a function of the character’s level, which are called Ability-Increasing Functions (AIFs). A well-balanced on-line RPG is characterized by having a set of well-balanced AIFs. In this paper, we propose an evolutionary design method, including integration with an improved Probabilistic Incremental Program Evolution (PIPE) and a Cooperative Coevolutionary Algorithm (CCEA), for on-line RPGs to maintain the game balance. Moreover, we construct a simplest turn-based game model and perform a series of experiments based on it. The results indicate that the proposed method is able to obtain a set of well-balanced AIFs efficiently. They also show that in this case the CCEA outperforms the simple genetic algorithm, and that the capability of PIPE has been significantly improved through the improvement work.

Optimization of the anaerobic treatment of a waste stream from an enhanced oil recovery process

The aim of this work was to optimize the anaerobic treatment of a waste stream from an enhanced oil recovery (EOR) process. The treatment of a simulated waste water containing about 150 mg chemical oxygen demand (COD)/L of total petroleum hydrocarbons (TPH) and the saturation level of CO2 was evaluated. A two-step anaerobic system was undertaken in the mesophilic temperature range (30-40°C). The method of evolutionary operation EVOP factorial design was used to optimize pH, temperature and organic loading rate with the target parameters of CO2 reduction and CH4 production in the first reactor and TPH removal in the second reactor. The results showed 98% methanogenic removal of CO2 and CH4 yield of 0.38 L/gCOD in the first reactor and 83% TPH removal in the second reactor. In addition to enhancing CO2 and TPH removal and CH4 production, application of this method showed the degree of importance of the operational variables and their interactive effects for the two reactors in series.

An Evolutionary Design Method Using Genetic Local Search Algorithm to Obtain Broad/Dual-Band Characteristics for Circular Polarization Slot Antennas

We present a new evolutionary design method using genetic local search (GLS) algorithm to obtain broad/dual-band characteristics for circular polarization (CP) slot antennas. By employing this computer-aided design (CAD) technique, optimal or near-optimal impedance and axial-ratio bandwidths for a given operating frequency can be achieved without using the trial and error method. In our method, several key parameters were first investigated, and their effects on both the input impedance and the axial-ratio were obtained by simulation. Different local search procedures used in the GLS algorithm were then developed. The proposed design technique was evaluated by designing antennas using different slot shapes: the ellipse, the equilateral triangle, and the square. These antenna prototypes were constructed and measured to provide validation of the design method.

Network design meets in silico evolutionary biology

Cell fate is programmed through gene regulatory networks that perform several calculations to take the appropriate decision. In silico evolutionary optimization mimics the way Nature has designed such gene regulatory networks. In this review we discuss the basic principles of these evolutionary approaches and how they can be applied to engineer synthetic networks. We summarize the basic guidelines to implement an in silico evolutionary design method, the operators for mutation and selection that iteratively drive the network architecture towards a specified dynamical behavior. Interestingly, as it happens in natural evolution, we show the existence of patterns of punctuated evolution. In addition, we highlight several examples of models that have been designed using automated procedures, together with different objective functions to select for the proper behavior. Finally, we briefly discuss the modular designability of gene regulatory networks and its potential application in biotechnology.

Bioinspired Computational Framework for Enhancing Creativity, Optimality, and Robustness in Design

This paper presents results of transdisciplinary research on the development of a bioinspired computational framework for engineering design. This framework is intended to support design by addressing three critical design objectives, including novelty, optimality, and robustness. It provides several computational models and methods, which are inspired by fundamental processes occurring in nature, and discusses their potential for enhancing design. They include models and methods for evolutionary, developmental, and coevolutionary design. Their use is illustrated with examples from the area of steel structural design ranging from a simple cantilever beam design problem to a much more complex problem of designing wind bracings in tall buildings. The paper also shows how several methods and models can be integrated and form a coherent bioinspired computational framework for engineering design.

The profession of ITEvolutionary system development

Large systems projects are failing at an alarming rate. It's time to take evolutionary design methods off the shelf .

Swarm grammars: growing dynamic structures in 3D agent spaces

We present a new way of dynamically growing and breeding structures in 3D space through swarming agents. Different agent types and the way they evolve over time is specified by a swarm grammar similar to Lindenmayer systems. We expand common L-system string interpretation from a single turtle to a multitude of turtles which behave like a swarm. By describing swarm agents within the framework of formal grammars, we build a bridge from symbolic production systems (rewrite systems) to three-dimensional real-time construction procedures that are executed by reactive and interacting agents which move in simulated physical 3D spaces. We introduce constructor agents, their formal representation in swarm grammars and demonstrate by examples how (1) the swarm rules, (2) the agent parameters and (3) the environ ment can influence the actual construction and growth processes that are initiated and directed by the swarms. In order to facilitate exploration of a large variety of swarm grammars, we apply interactive evolutionary design methods to create swarm grammar sculptures and 3D structures.

The Architecture and Circuital Implementation Scheme of a New Cell Neural Network for Analog Signal Processing

It is a difficult problem that using cellular neural network to make up of analog signal processing circuit. This paper presented the architecture of new cellular neural network SCCNN for analog signal processing circuits, designed the neural cell circuit, and developed the evolutionary design method of the SCCNN based on self- adapting genetic algorithm. In the architecture of new cellular neural network SCCNN, each neural cell connects with four neighborhood neural cells, the neural cell circuit and signal transfer line between neural cells are controlled by programmable switches. The validity of the SCCNN architecture and the evolutionary design method are verified through digital simulation. The experimental results indicate that the SCCNN hard- ware is a universal cellular neural network for analog signal processing circuit, which can be used to make up of the analog signal amplifier, analog signal filter, digit logic circuit, DAC circuit and so on.

A versatile software tool for microwave planar radar absorbing materials design using global optimization algorithms

A computer-aided design (CAD) tool for the design of planar multi-layer coatings with high absorption for a desired frequency and angle range is presented. The tool uses deterministic and evolutionary optimization design methods. Both single and multi-objective design algorithms can be used and a single absorber design or the Pareto front can be found accordingly. A novel design technique utilizing PSO is also presented. A user-defined or a pre-defined design case can be selected interchangeably. The choice of selecting materials from pre-defined database is also available. The tool can be useful for both educational and research purposes. The efficiency of the tool is demonstrated through several design cases that are in agreement with existing literature data.