Browsing by Subject "Genetic algorithms."

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Open Access
Approximating small open economy models with neural network trained by genetic algorithm
(2008) Coşkun, Yeşim
This thesis work presents a direct numerical solution methodology to approximate the small open economy models with debt elastic interest rate premium and with convex portfolio adjustment cost, both studied by Stephanie SchmittGroh´e and Martin Uribe(2003). This recent method is compared with the firstorder approximation to the policy function from the aspect of second moments of endogenous variables and their impulse responses. The proposed methodology, namely genetic algorithm-neural network (GA-NN), parameterizes the policy function with a feed-forward neural network that is trained by a genetic algorithm. Thus, unlike the first-order approximation, GA-NN does not require the continuity and the existence of derivatives of objective and policy functions. Importantly, since genetic algorithm is an evolutionary algorithm that enables global search over the feasible set, it provides a robust result in any solution space. Also GA-NN method gives not only the moments of the model but also the optimal path.
Open Access
Assembly line balancing using genetic algorithms
(1997) Tanyer, Muzaffer
For the last few decades, the genetic algorithms (GAs) have been used as a kind of heuristic in many areas of manufacturing. Facility layout, scheduling, process planning, and assembly line balancing are some of the areas where GAs are already popular. GAs are more efficient than traditional heuristics and also more flexible as they allow substantial changes in the problem’s constraints and in the solution approach with small changes in the program. For this reason, GAs attract the attention of both the researchers and practitioners. Chromosome structure is one of the key components of a GA. Therefore, in this thesis, we focus on the special structure of the assembly line balancing px'oblem and design a chromosome structure that operates dynamically. We propose a new mechanism to work in parallel with GAs, namely dynamic partitioning. Different from many other GA researchers, we particularly compare different population re\asion mechanisms and the effect of elitism on these mechanisms. Elitism is revised by the simulated annealing idea and various levels of elitism are created and their effects are observed. The proposed GA is £ilso compared with the traditional heuristics.