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.

Construction of strong equality index from implication operators

We present the concept of strong equality index, staring from the definition of strong inclusion given by Dubois and Prade in 1980, We also present a construction method based on the use of implication operators and two specific properties of the implications.

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.

Multi-objective image segmentation with an interactive evolutionary computation approach

In this paper, a multi-objective image segmentation approach with an Interactive Evolutionary Computation (IEC)-based framework is presented. Two objectives, i.e., the overall deviation and the connectivity measure, are optimized simultaneously using a mu

Image reduction operators based on non-monotonic averaging functions

Image reduction is a crucial task in image processing, underpinning many practical applications. This work proposes novel image reduction operators based on non-monotonic averaging aggregation functions. The technique of penalty function minimisation is used to derive a novel mode-like estimator capable of identifying the most appropriate pixel value for representing a subset of the original image. Performance of this aggregation function and several traditional robust estimators of location are objectively assessed by applying image reduction within a facial recognition task. The FERET evaluation protocol is applied to confirm that these non-monotonic functions are able to sustain task performance compared to recognition using nonreduced images, as well as significantly improve performance on query images corrupted by noise. These results extend the state of the art in image reduction based on aggregation functions and provide a basis for efficiency and accuracy improvements in practical computer vision applications.

Consistency and stability in aggregation operators : an application to missing data problems

In this work we analyze the key issue of the relationship that should hold between the operators in a family {An} of aggregation operators in order to understand they properly define a consistent whole. Here we extend some of the ideas about stability of a family of aggregation operators into a more general framework, formally defining the notions of i – L and j – R strict stability for families of aggregation operators. The notion of strict stability of order k is introduced as well. Finally, we also present an application of the strict stability conditions to deal with missing data problems in an information aggregation process. For this analysis, we have focused in the weighted mean family and the quasi-arithmetic weighted means families.