A GA-PSO layered encoding evolutionary approach to 0/1 knapsack optimization

This paper presents a layered encoding cascade evolutionary approach to solve a 0/1 knapsack optimization problem. A layered encoding structure is proposed and developed based on the schema theorem and the concepts of cascade correlation and multi-population evolutionary algorithms. Genetic algorithm (GA) and particle swarm optimization (PSO) are combined with the proposed layered encoding structure to form a generic optimization model denoted as LGAPSO. In order to enhance the finding of both local and global optimum in the evolutionary search, the model adopts hill climbing evaluation criteria, feature of strength Pareto evolutionary approach (SPEA) as well as nondominated spread lengthen criteria. Four different sizes benchmark knapsack problems are studied using the proposed LGAPSO model. The performance of LGAPSO is compared to that of the ordinary multi-objective optimizers such as VEGA, NSGA, NPGA and SPEA. The proposed LGAPSO model is shown to be efficient in improving the search of knapsack’s optimum, capable of gaining better Pareto trade-off front.

An interactive evolutionary algorithm for MMIC low noise amplifier design

A layer-encoded interactive evolutionary algorithm (IEA) for optimization of design parameters of a monolithic microwave integrated circuit (MMIC) low noise amplifier is presented. The IEA comprises a combination of the genetic algorithm (GA) and the particle swarm optimization (PSO) technique. The layer-encoding structure allows human intervention in order to accelerate the process of evolution, whereas the GA and PSO technique are incorporated to enhance both global and local searches. With this combination of features, the proposed IEA has shown to be efficient in meeting all requirements and constraints of the MMIC. In addition, the IEA is able to optimize noise figure, current, and power gain of the MMIC amplifier design.

Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification

In this paper, an Evolutionary Artificial Neural Network (EANN) that combines the Fuzzy ARTMAP (FAM) network and a Hybrid Evolutionary Programming (HEP) model is introduced. The proposed FAM-HEP model, which combines the strengths of FAM and HEP, is able to construct its network structure autonomously as well as to perform learning and evolutionary search and adaptation concurrently. The effectiveness of the proposed FAM-HEP network is assessed empirically using several benchmark data sets and a real medical diagnosis problem. The performance of FAM-HEP is analyzed, and the results are compared with those of FAM-EP, FAM, and other classification models. In general, the results of FAM-HEP are better than those of FAM-EP and FAM, and are comparable with those from other classification models. The study also reveals the potential of FAM-HEP as an innovative EANN model for undertaking pattern classification problems in general, and a promising computerized decision support tool for tackling medical diagnosis tasks in particular.

Introduction to evolutionary computing in system design

In this chapter, an introduction on the use of evolutionary computing techniques, which are considered as global optimization and search techniques inspired from biological evolutions, in the domain of system design is presented. A variety of evolutionary computing techniques are first explained, and the motivations of using evolutionary computing techniques in tackling system design tasks are then discussed. In addition, a number of successful applications of evolutionary computing to system design tasks are described.

An evolutionary-based similarity reasoning scheme for monotonic multi-input fuzzy inference systems

In this paper, an Evolutionary-based Similarity Reasoning (ESR) scheme for preserving the monotonicity property of the multi-input Fuzzy Inference System (FIS) is proposed. Similarity reasoning (SR) is a useful solution for undertaking the incomplete rule base problem in FIS modeling. However, SR may not be a direct solution to designing monotonic multi-input FIS models, owing to the difficulty in getting a set of monotonically-ordered conclusions. The proposed ESR scheme, which is a synthesis of evolutionary computing, sufficient conditions, and SR, provides a useful solution to modeling and preserving the monotonicity property of multi-input FIS models. A case study on Failure Mode and Effect Analysis (FMEA) is used to demonstrate the effectiveness of the proposed ESR scheme in undertaking real world problems that require the monotonicity property of FIS models.

A call for an expanded synthesis of developmental and evolutionary paradigms

Charney's target article continues a critique of genetic blueprint models of development that suggests reconsideration of concepts of adaptation, inheritance, and environment, which can be well illustrated in current research on infant attachment. The concepts of development and adaptation are so heavily based on the model of genetics and inheritance forged in the modern synthesis that they will require reconsideration to accommodate epigenetic inheritance.

An empirical case for introductory psychology tutorials in a large-class format

This study examined the effectiveness of an instructional format that Involved conducting introductory psychology tutorials in large conventional lecture theatres with over 100 students per class. We maximised the use of skilled tutors, sharing of student perspectives, and cooperative learning in delivering interactive, active learning activities, Students (N = 284) within each class were randomly assigned to smaller groups that were scaled within the same large class environment. Students reported positive perceptions of their learning experience at an end-of-semester survey. Moreover, they performed significantly better in a major assessment on the tutorial component than a previous cohort taught in conventional small tutorial classes. Our finding indicate that active learning techniques can be implemented just as effectively in a large class tutorial format. These findings have practical implications for designing cost effective yet pedagogically vigorous instructional formats for introductory psychology and other liberal arts courses.

Training postgraduate psychology students to deliver psychological services online

Information and communication technologies are increasingly being used to remotely deliver psychological services. This delivery method confers clear advantages to both client and therapist, including the accessibility of services by otherwise unserved populations and cost-effective treatment. Remote services can be delivered in a real-time or delayed manner, providing clients with a wealth of therapy options not previously available. The proliferation of these services has outstripped the development and implementation of all but the most rudimentary of regulatory frameworks, potentially exposing clients to substandard psychological services. Integrating mandatory training on the delivery of online psychological services into accredited postgraduate psychology courses would aid in addressing this issue. The purpose of this article is to outline issues of consideration in the development and implementation of such a training programme. An online etherapy training programme developed by Swinburne University's National eTherapy Centre will be used as an example throughout.