Exploring the Performance of an Evolutionary Algorithm for Greenhouse Control

Evolutionary algorithms for optimization of dynamic problems have recently received increasing attention. Online control is a particularly interesting class of dynamic problems, because of the interactions between the controller and the controlled system. In this paper, we report experimental results on two aspects of the direct control strategy in relation to a crop-producing greenhouse. In the first set of experiments, we investigated how to balance the available computation time between population size and generations. The second experiments were on different control horizons, and showed the importance of this aspect for direct control. Finally, we discuss the results in the wider context of dynamic optimization

Towards Better Integration of Surrogate Models and Optimizers

Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been proven to be very effective in solving (synthetic and real-world) computationally expensive optimization problems with a limited number of function evaluations. The two main components of SAEAs are: the surrogate model and the evolutionary optimizer, both of which use parameters to control their respective behavior. These parameters are likely to interact closely, and hence the exploitation of any such relationships may lead to the design of an enhanced SAEA. In this chapter, as a first step, we focus on Kriging and the Efficient Global Optimization (EGO) framework. We discuss potentially profitable ways of a better integration of model and optimizer. Furthermore, we investigate in depth how different parameters of the model and the optimizer impact optimization results. In particular, we determine whether there are any interactions between these parameters, and how the problem characteristics impact optimization results. In the experimental study, we use the popular Black-Box Optimization Benchmarking (BBOB) testbed. Interestingly, the analysis finds no evidence for significant interactions between model and optimizer parameters, but independently their performance has a significant interaction with the objective function. Based on our results, we make recommendations on how best to configure EGO

This document in subdirectoryDS/03/6/ Models for Evolutionary Algorithms and Their Applications in System Identification and Control Optimization

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Multinational Evolutionary Algorithms

Since practical problems often are very complex with a large number of objectives it can be difficult or impossible to create an objective function expressing all the criterias of good solutions. Sometimes a simpler function can be used where local optimas could be both valid and interesting. Because evolutionary algorithms are population -based they have the best potential for finding more of the best solutions among the possible solutions. However, standard EAs often converge to one solution and leave therefore only this single option for a final human selection. So far at least two methods, sharing and tagging, have been proposed to solve the problem. This paper presents a new method for finding more quality solutions, not only global optimas but local as well. The method tries to adapt its search strategy to the problem by taking the topology of the fitness landscape into account. The idea is to use the topology to group the individuals into sub-populations each covering a part o..

Genetic Programming with Smooth Operators for Arithmetic Expressions: Diviplication and Subdition

This paper introduces the smooth operators for arithmetic expressions as an approach to smoothening the search space in Genetic Program- ming (GP). Smooth operator GP interpolates between arithmetic operators such as * and /, thereby allowing a gradual adaptation to the problem. The suggested approach is compared to traditional GP on a system identification problem