abstract: although all university majors are prominent and the necessity of their presences is of no question, they might not have the same priority basis considering different resources and strategies that could be spotted for a country. this paper focuses on clustering and ranking university majors in iran. to do so, a model is presented to clarify the procedure. eight different criteria are ...

Based on the characteristics of perishable goods, we extend the location-routing problem with distance constraints by adopting chance constrained goal programming approach to solve the uncertainty and to express the different priority levels of decisions. The random of depot construction costs and vehicle routing travel time is taken into account. Meanwhile, a two-stage genetic algorithm incorp...

This chapter describes a subarea of machine learning which is actively exploring the use of genetic algorithms as the key element in the design of robust learning strategies. After charac­ terizing the kinds of learning problems motivating this approach, a brief overview of genetic algorithms is presented. Three major approaches to using genetic algorithms for machine learn­ ing are described a...

in practice, obtaining the global optimum for the economic dispatch {bf (ed)}problem with ramp rate limits and prohibited operating zones is presents difficulties. this paper presents a new andefficient method for solving the economic dispatch problem with non-smooth cost functions using afuzzy adaptive genetic algorithm (faga). the proposed algorithm  deals  with the issue ofcontrolling the ex...

We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We introduce an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs so far. Then, we synthesize new programs and add them to the priority queue by samp...

We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs so far. Then, we synthesize new programs and add them to the priority queue by samplin...