An Interactive Genetic Algorithm system is proposed for designing a car silhouette while involving the style designer in the evaluation process of a population of individuals. This IGA is based on the principle of an indirect encoding of a closed curve genotype using a primary Fourier decomposition. A crossing over operator is proposed for mixing the parents’ genes by a random weighted average into a new child’s genotype. A perceived similarity index between two genotypes is built to check that our IGA is able to converge toward a target individual starting from the genes of an initial population.

SUMMARYWith the growing complexity of industrial software applications, industrials are looking for efficient and practical methods to validate the software. This paper develops a model‐based statistical testing approach that automatically generates online and offline test cases for embedded software. It discusses an integrated framework that combines solutions for three major software testing research questions: (i) how to select test inputs; (ii) how to predict the expected results of a test; and (iii) when to stop testing software. The automatic selection of test inputs is based on a stochastic test model that accounts for the main particularity of embedded software: time sensitivity. Software test practitioners may design one or more test models when they generate random, user‐oriented, or fault‐oriented test inputs. A formal framework integrating existing and appropriate specification techniques was developed for the design of automated test oracles (executable software specifications) and the formal measurement of functional coverage. The decision to stop testing software is based on both test coverage objectives and cost constraints. This approach was tested on two representative case studies from the automotive industry. The experiment was performed at unit testing level in a simulated environment on a host personal computer (automatic test execution). The two software functionalities tested had previously been unit tested and validated using the test design approach conventionally used in the industry. Applying the proposed model‐based statistical testing approach to these two case studies, we obtained significant improvements in performing functional unit testing in a real and complex industrial context: more bugs were detected earlier and in a shorter time. Copyright © 2012 John Wiley & Sons, Ltd.

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Starting from a need and a set of functional requirements (FRs), a designer is often perplexed to assess the potential of a given concept to fit these requirements. He is even more perplexed when several concepts are candidates. This paper proposes a definition of a concept in a practical way as a parameterized model linking a set of design variables (DVs) to a set of performance variables (PVs). This set of PVs is supposed to be the same for any concept candidate to fulfill a need. This is why our model propose to “plug” a card of FRs into candidate concepts in order to lead concurrent reasonings on competing concepts until one or several of them appear to be of poor interest. The plugging mechanism is implemented by constraint programming techniques (evolved interval arithmetics) that immediately contract the performance and design variable domains to provide an outer approximation of the solution (or design) space. Two sets of comparison operators between solution spaces are proposed: operators for comparing the relative potential of two concepts submitted to the same FRs, and operators for comparing two successive stages of solution spaces of a given concept. These last operators provide the way to tackle the robustness of design decision making under uncertainty. All the mentioned features: plugging mechanism, contraction of domains and design space representation, comparison operators and robustness considerations have been experimented on an example of a pair of candidate concepts of truss structures.