Semantic variation operators for multidimensional genetic programming

Multidimensional genetic programming represents candidate solutions as sets of programs, and thereby provides an interesting framework for exploiting building block identification. Towards this goal, we investigate the use of machine learning as a way to bias which components of programs are promoted, and propose two semantic operators to choose where useful building blocks are placed during crossover. A forward stagewise crossover operator we propose leads to significant improvements on a set of regression problems, and produces state-of-the-art results in a large benchmark study. We discuss this architecture and others in terms of their propensity for allowing heuristic search to utilize information during the evolutionary process. Finally, we look at the collinearity and complexity of the data representations that result from these architectures, with a view towards disentangling factors of variation in application.Comment: 9 pages, 8 figures, GECCO 201

Optimization of Coastal Cruise Lines in China

The paper analyzes the current state of the Chinese cruise market and presents the idea of building a business model of coastal cruising. The cruise demand of middle-income families, which includes the desired travel days, ports of call, is surveyed. The data of the previous non-cruise travels and the data of future cruises of middle-income families are used to develop a model designed to identify the maximum passenger volume with minimum operating costs while taking cruise itineraries and schedules into account. A matrix coding genetic algorithm was designed to solve the model. The case study found that a voyage of 4.79 days results in equilibrium, that the annual demand is 200,840 passengers, and that the daily voyage cost is 0.843 million Yuan

Optimization of Coastal Cruise Lines in China

The paper analyzes the current state of the Chinese cruise market and presents the idea of building a business model of coastal cruising. The cruise demand of middle-income families, which includes the desired travel days, ports of call, is surveyed. The data of the previous non-cruise travels and the data of future cruises of middle-income families are used to develop a model designed to identify the maximum passenger volume with minimum operating costs while taking cruise itineraries and schedules into account. A matrix coding genetic algorithm was designed to solve the model. The case study found that a voyage of 4.79 days results in equilibrium, that the annual demand is 200,840 passengers, and that the daily voyage cost is 0.843 million Yuan

Fuel Consumption Minimization Procedure of Sail-assisted Motor Vessel based on a Systematic Meshing of the Explored Area

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