Browsing by Subject "multi-objective optimization"

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Access status: Restricted ,
Evolutionary techniques of investment portfolio construction
(Data obrony: 2014-10-29) Kocot, Mikołaj
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Open Access ,
Multi-objective optimization of vehicle routing problem using evolutionary algorithm with memory
(Wydawnictwa AGH, 2017) Podlaski, Krzysztof; Wiatrowski, Grzegorz
The idea of a new evolutionary algorithm with memory aspect included is proposed to find multiobjective optimized solution of vehicle routing problem with time windows. This algorithm uses population of agents that individually search for optimal solutions. The agent memory incorporates the process of learning from the experience of each individual agent as well as from the experience of the population. This algorithm uses crossover operation to define agents evolution. In the paper we choose as a base the Best Cost Route Crossover (BCRC) operator. This operator is well suited for VPRTW problems. However it does not treat both of parent symmetrically what is not natural for general evolutionary processes. The part of the paper is devoted to find an extension of the BCRC operator in order to improve inheritance of chromosomes from both of parents. Thus, the proposed evolutionary algorithm is implemented with use of two crossover operators: BCRC and its extended-modified version. We analyze the results obtained from both versions applied to Solomon’s and Gehring & Homberger instances. We conclude that the proposed method with modified version of BCRC operator gives statistically better results than those obtained using original BCRC. It seems that evolutionary algorithm with memory and modification of Best Cost Route Crossover Operator lead to very promising results when compared to the ones presented in the literature.
Access status: Open Access ,
Survey on multi-objective-based parameter optimization for deep learning
(Wydawnictwa AGH, 2023) Chakraborty, Mrittika; Pal, Wreetbhas; Bandyopadhyay, Sanghamitra; Maulik, Ujjwal
Deep learning models form some of the most powerful machine-learning models for the extraction of important features. Most of the designs of deep neural models (i.e., the initialization of parameters) are still manually tuned, hence, obtaining a model with high performance is exceedingly time-consuming and occasionally impossible. Optimizing the parameters of deep networks, therefore requires improved optimization algorithms with high convergence rates. The single objective-based optimization methods that are generally used are mostly time-consuming and do not guarantee optimum performance in all cases. Mathematical optimization problems that contain multiple objective functions that must be optimized simultaneously fall under the category of multi-objective optimization (sometimes referred to as Pareto optimization). Multi-objective optimization problems form one of the alternatives yet useful options for parameter optimization, however, this domain is a bit underexplored. In this survey, we focus on exploring the effectiveness of multi-objective optimization strategies for parameter optimization in conjunction with deep neural networks. The case studies that are used in this study focus on how the two methods are combined to provide valuable insights into the generation of predictions and analysis in multiple applications.