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Received (received date) Revised (revised date) Many real-world problems involve measures of objectives that may be dynamically optimized. The application of evolutionary algorithms in time dependant optimization is currently receiving growing interest as potential applications are numerous ranging from mobile robotics to real time process command.(More)
Time dependent optimization has revealed to be a promising gap for the entire Genetic Algorithms community since it has numerous applications. This paper extends previous work related to the use of meta-genes ((5]) in the so-called Dual Genetic Algorithms (DGAs). A more generic framework, involving a variable number of genes, is introduced. Folding Genetic(More)
This descriptive study discusses two conceptual difficulties encountered by students in introductory programming courses regardless of the chosen language or pedagogical approach (e.g. objects, classes or fundamentals first). Firstly, students tend to learn programming by memorizing correct code examples instead of acquiring the programming thought process.(More)
This paper explores the relationship between new constructivist apprenticeship techniques meant to improve programming pedagogy [6][7] and student self-direction. To this end, we used the lens of the Personal Responsibility Orientation [2] to measure the impact on student self-efficacy and self direction of our interventions. These learning activities were(More)
Over the past years, many problems related to the system administration of laboratories for undergraduate system-oriented courses have found elegant solutions in the deployment of virtualization suites. This technological advance enabled these courses to switch from a mostly descriptive content to learning activities which engage students in hands-on,(More)
Techniques such as Pair Programming, or allowing students to run their programs against a reference test harness, have demonstrated their effectiveness in improving grades or retention rates. This paper proposes to supplement the existing literature by investigating students' perceptions of the benefits of writing tests, working with other students and(More)
A DGA is a genetic algorithm with novel features: relational schemata. These structures allow a more natural expression of relations existing between loci. Indeed, schemata in standard genetic algorithms can only specify values for each locus. Relational schemata are based on the notion of duality: a schema can be represented by two strings. The intent of(More)
This paper revisits past works on fitness distance correlation (FDC) in relation to genetic algorithms (GA) performance, and puts forth evidence that this statistical measure is relevant to predict the performance of a GA. We propose an interpretation of Hamming-distance based FDC, which takes into account the GA dynamics and the effects of crossover(More)