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.

A comparative analysis of decision trees vis-à-vis other computational data mining techniques in automotive insurance fraud detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of - financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists.

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.

Does moral cleansing moderate the effect of evolutionary altruism on helping intention? An exploratory study

There is strong evidence for social evolutionary motivations for helping (e.g., reciprocal altruism) and also growing support for the influence of the social cognitive theory of moral cleansing on prosociality. Where the former motivation is interpersonal, the latter is intrapersonal. This experimental study hypothesized that, in addition to main effects of evolutionary altruism and moral cleansing on helping intention, an interaction would occur between these theoretical motivations. Using three situational helping scenarios as dependent measures, the effect of participants’ morally-valenced recalled behavior (moral/immoral/achievement/failure) and the effect of their social proximity to a helping target (cousin/colleague/stranger) on helping intention was determined. Overall, 616 Australian participants (90.1% female) completed the online experiment. Two-way ANOVA demonstrated a consistent main effect of social proximity on helping intention across all three helping scenarios, supporting evolutionary social psychological explanations for helping. However, instead of moral self-regulation effects, moral identity consistency effects were induced by the moral behavior recall manipulation. A main effect of behaviour recall on helping intention occurred, with moral recall increasing helping intention. The problem of theoretical ambiguity regarding moral identity consistency and moral self-regulation is discussed, as is the useful role of null result publications in informing effective experimental design.

The descent of Morgan in Australia: kinship representation from the Australian colonies

Morgan had two extraordinary disciples in Lorimer Fison and Alfred Howitt in Australia. They were inspired by Morgan’s kinship schedule and were profoundly engaged in the method and theory of the collection of kinship data and its interpretation. Fison began using the schedule in Fiji in 1869. Soon after his first contact with Howitt, in 1873, they changed the method of collection of kinship terminologies. This paper traces the shift from tabulated kinship lists to family trees and the use of sticks to represent relationships (nearly twenty years before Rivers’ celebrated ‘genealogical method’), as well as efforts to find new means of representing kinship through experimentation with ‘ graphic formulae’ inspired by chemical equations. These innovations first occurred through the gathering of kinship data about the Kŭnai of Gippsland, Victoria, and crucially involved close collaboration between Howitt and his Kŭnai consultant Tulaba. What was revealed in this process was an indigenous kinship system quite different from that found in other parts of colonial Australia known at the time. Fison and Howitt explained this system as transitional between two stages in terms of Morgan’s evolutionary scheme, but at the same time challenged the assumption that the general scheme could be applied to Australia. While the details of Morgan’s evolutionary stages have faded from view, the methods of collection, representation, transmission, comparison and interpretation of kinship data are still live issues in anthropology today. The kind of kinship system discovered in Gippsland involved neutralisation of the cross-parallel distinctions, distinctions that are otherwise typical of Australia. Such neutralisation can now be shown to occur elsewhere in Australia. There does indeed seem to have been a transition from a Dravidianate system with cross-parallel distinctions to ‘overlays’ of cross-parallel neutralisation, and finally a complete loss in some generations of such distinctions in the terminology. These discoveries open up possibilities of rebuilding a diachronic theory of kinship change and evolution, incorporating some of the insights of Fison and Howitt, though without their specific hypotheses, either of local developments in Gippsland or the grand scheme of Morgan.