18,316 research outputs found
An application of Preference-Inspired Co-Evolutionary Algorithm to sectorization
Sectorization problems have significant challenges arising from the many objectives that must be optimised simultaneously. Several methods exist to deal with these many-objective optimisation problems, but each has its limitations. This paper analyses an application of Preference Inspired Co-Evolutionary Algorithms, with goal vectors (PICEA-g) to sectorization problems. The method is tested on instances of different size difficulty levels and various configurations for mutation rate and population number. The main purpose is to find the best configuration for PICEA-g to solve sectorization problems. Performancemetrics are used to evaluate these configurations regarding the solutions’ spread, convergence, and diversity in the solution space. Several test trials showed that big and medium-sized instances perform better with low mutation rates and large population sizes. The opposite is valid for the small size instances.info:eu-repo/semantics/acceptedVersio
Modeling preference time in middle distance triathlons
Modeling preference time in triathlons means predicting the intermediate
times of particular sports disciplines by a given overall finish time in a
specific triathlon course for the athlete with the known personal best result.
This is a hard task for athletes and sport trainers due to a lot of different
factors that need to be taken into account, e.g., athlete's abilities, health,
mental preparations and even their current sports form. So far, this process
was calculated manually without any specific software tools or using the
artificial intelligence. This paper presents the new solution for modeling
preference time in middle distance triathlons based on particle swarm
optimization algorithm and archive of existing sports results. Initial results
are presented, which suggest the usefulness of proposed approach, while remarks
for future improvements and use are also emphasized.Comment: ISCBI 201
Some Computational Aspects of Essential Properties of Evolution and Life
While evolution has inspired algorithmic methods of heuristic optimisation, little has been done in the way of using concepts of computation to advance our understanding of salient aspects of biological evolution. We argue that under reasonable assumptions, interesting conclusions can be drawn that are of relevance to behavioural evolution. We will focus on two important features of life--robustness and fitness optimisation--which, we will argue, are related to algorithmic probability and to the thermodynamics of computation, subjects that may be capable of explaining and modelling key features of living organisms, and which can be used in understanding and formulating algorithms of evolutionary computation
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