Abstract-Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In this paper, a "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator. The relationship between FEP and classical EP (CEP) is similar to that between fast simulated annealing and the classical version. Both analytical and empirical studies have been carried out to evaluate the performance of FEP and CEP for different function optimization problems. This paper shows that FEP is very good at search in a large neighborhood while CEP is better at search in a small local neighborhood. For a suite of 23 benchmark problems, FEP performs much better than CEP for multimodal functions with many local minima while being comparable to CEP in performance for unimodal and multimodal functions with only a few local minima. This paper also shows the relationship between the search step size and the probability of finding a global optimum and thus explains why FEP performs better than CEP on some functions but not on others. In addition, the importance of the neighborhood size and its relationship to the probability of finding a near-optimum is investigated. Based on these analyses, an improved FEP (IFEP) is proposed and tested empirically. This technique mixes different search operators (mutations). The experimental results show that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.Index Terms-Cauchy mutations, evolutionary programming, mixing operators.

In evolutionary optimization, it is important to understand how fast evolutionary algorithms converge to the optimum per generation, or their convergence rates. This paper proposes a new measure of the convergence rate, called the average convergence rate. It is a normalized geometric mean of the reduction ratio of the fitness difference per generation. The calculation of the average convergence rate is very simple and it is applicable for most evolutionary algorithms on both continuous and discrete optimization. A theoretical study of the average convergence rate is conducted for discrete optimization. Lower bounds on the average convergence rate are derived. The limit of the average convergence rate is analyzed and then the asymptotic average convergence rate is proposed.authorsversionPeer reviewe

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Past research provides evidence of a wide range of antecedent variables to explain new product development performance. The reported determinants are a combination of strategic, development process, organizational, and market factors. However, these research efforts are fragmented and focus on one or two key factors. Researchers look at this issue from a narrow disciplinary perspective. So the results are quite discipline-biased. Apart from the lack of a multidisciplinary approach, there is a real gap in subjecting these factors to rigorous empirical tests, and incorporate modern proven best practices of world class innovative companies to guide practitioners in their innovation journey. There is a real need to broaden the range of factors considered and develop an integrated model with high predictive power to guide new product development (NPD) efforts. This paper aims to address this research issue. In this research study, four key factors have been identified in modelling financial performance in NPD. They are strategic fit and shared vision, market orientation, stimulus and slack time, organization for collaboration and communication. Regression analysis was performed to validate their importance and estimate the parameters of these four key factors. The result shows that all these four factors are statistically significant in determining financial performance in NPD. This framework can be used practically to assist managers to improve in their NPD efforts so as to achieve higher financial return on investment.

The purpose of this paper is to show how the concepts of collaborative agents and artificial neural networks (ANNs) can work together to enable collaborative supply chain planning (SCP). An agent-based supply chain network is decomposed into multiple ANNs in a way that the actual customer requirements and the agents' goals and constraints are matched in different stages. An error-minimising algorithm which models the agents' collaboration mechanism is used to train three ANNs, namely the supply net, the production net and the delivery net, for pursuing complete order fulfilment across the supply chain. In the example problem, the collaborative SCP paradigm is applied to determine the supply plan of an alliance of small firms, which provides assemble-to-order goods with short delivery lead-time to a regional market. The calculation results showed that the ANN approach achieved complete order fulfilment and significantly increased the resource utilisation of all supply chain agents.

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