A general framework of multi-population methods with clustering in undetectable dynamic environments

Copyright @ 2011 IEEETo solve dynamic optimization problems, multiple population methods are used to enhance the population diversity for an algorithm with the aim of maintaining multiple populations in different sub-areas in the fitness landscape. Many experimental studies have shown that locating and tracking multiple relatively good optima rather than a single global optimum is an effective idea in dynamic environments. However, several challenges need to be addressed when multi-population methods are applied, e.g., how to create multiple populations, how to maintain them in different sub-areas, and how to deal with the situation where changes can not be detected or predicted. To address these issues, this paper investigates a hierarchical clustering method to locate and track multiple optima for dynamic optimization problems. To deal with undetectable dynamic environments, this paper applies the random immigrants method without change detection based on a mechanism that can automatically reduce redundant individuals in the search space throughout the run. These methods are implemented into several research areas, including particle swarm optimization, genetic algorithm, and differential evolution. An experimental study is conducted based on the moving peaks benchmark to test the performance with several other algorithms from the literature. The experimental results show the efficiency of the clustering method for locating and tracking multiple optima in comparison with other algorithms based on multi-population methods on the moving peaks benchmark

Genetic algorithms applied to asset & liability management

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementEffective asset liability management is at the core of what a life insurance company must do, particularly in what concerns defined benefits pension fund products. The life insurer faces a complex problem whereby multiple and sometimes conflicting objectives must be addressed at the same time, such as achieving higher returns while reducing the portfolio’s exposure to a plethora of risks. To achieve these goals, pension fund managers must then carefully choose asset allocation strategies for their portfolios from an infinite pool of asset combinations and weights. Given the nature of this problem, the use of genetic algorithms seems to be adequate, as this method is particularly well suited to deal with very large and multi-modal solution spaces. The main purpose of this dissertation is to assess how well the genetic algorithm method performs in solving this specific problem, and compare the results with other simpler methods. The results of Genetic Algorithms application were satisfactory and the results of this study suggests that Genetic Algorithms are a useful tool to solve ALM problems

A RELIABLE ROUTING MECHANISM WITH ENERGY-EFFICIENT NODE SELECTION FOR DATA TRANSMISSION USING A GENETIC ALGORITHM IN WIRELESS SENSOR NETWORK

Energy-efficient and reliable data routing is critical in Wireless Sensor Networks (WSNs) application scenarios. Due to oscillations in wireless links in adverse environmental conditions, sensed data may not be sent to a sink node. As a result of wireless connectivity fluctuations, packet loss may occur. However, retransmission-based approaches are used to improve reliable data delivery. These approaches need a high quantity of data transfers for reliable data collection. Energy usage and packet delivery delays increase as a result of an increase in data transmissions. An energy-efficient data collection approach based on a genetic algorithm has been suggested in this paper to determine the most energy-efficient and reliable data routing in wireless sensor networks. The proposed algorithm reduced the number of data transmissions, energy consumption, and delay in network packet delivery. However, increased network lifetime. Furthermore, simulation results demonstrated the efficacy of the proposed method, considering the parameters energy consumption, network lifetime, number of data transmissions, and average delivery delay

Addressing current challenges in cancer immunotherapy with mathematical and computational modeling

The goal of cancer immunotherapy is to boost a patient's immune response to a tumor. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumor microenvironment, immune-modulating effects of conventional treatments, and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modeling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumor classification, optimal treatment scheduling, and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modelers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumor-immune biology. We conclude the review with recommendations for modelers both with respect to methodology and biological direction that might help keep modelers at the forefront of cancer immunotherapy development.Comment: Accepted for publication in the Journal of the Royal Society Interfac

Investigating Genetic Algorithm Optimization Techniques in Video Games

Immersion is essential for player experience in video games. Artificial Intelligence serves as an agent that can generate human-like responses and intelligence to reinforce a player’s immersion into their environment. The most common strategy involved in video game AI is using decision trees to guide chosen actions. However, decision trees result in repetitive and robotic actions that reflect an unrealistic interaction. This experiment applies a genetic algorithm that explores selection, crossover, and mutation functions for genetic algorithm implementation in an isolated Super Mario Bros. pathfinding environment. An optimized pathfinding AI can be created by combining an elitist selection strategy with a uniform distribution crossover and minimal mutation rate

A semi-Markov model for stroke with piecewise-constant hazards in the presence of left, right and interval censoring.

This paper presents a parametric method of fitting semi-Markov models with piecewise-constant hazards in the presence of left, right and interval censoring. We investigate transition intensities in a three-state illness-death model with no recovery. We relax the Markov assumption by adjusting the intensity for the transition from state 2 (illness) to state 3 (death) for the time spent in state 2 through a time-varying covariate. This involves the exact time of the transition from state 1 (healthy) to state 2. When the data are subject to left or interval censoring, this time is unknown. In the estimation of the likelihood, we take into account interval censoring by integrating out all possible times for the transition from state 1 to state 2. For left censoring, we use an Expectation-Maximisation inspired algorithm. A simulation study reflects the performance of the method. The proposed combination of statistical procedures provides great flexibility. We illustrate the method in an application by using data on stroke onset for the older population from the UK Medical Research Council Cognitive Function and Ageing Study

Evolutionary biology for the 21st century

New theoretical and conceptual frameworks are required for evolutionary biology to capitalize on the wealth of data now becoming available from the study of genomes, phenotypes, and organisms - including humans - in their natural environments.Molecular and Cellular BiologyOrganismic and Evolutionary Biolog

Learning and adaptation strategies for evolving artifact capabilities

In this study we address enhancing the ability of social agents embedded in multi-agent based simulations to achieve their goals by using objects in their environment as artifacts. Reformulated as a discrete optimization problem solved with evolutionary computation methods, social agents are empowered to learn and adapt through observations of their own behavior, others in the environment and their community at large. An implemented case study is provided incorporating the model into the multi-agent simulation of the Village EcoDynamics Project developed to study the early Pueblo Indian settlers from A.D. 600 to 1300. Eliminating the existing presumption that agents automatically know the productivity of the landscape as part of their settling and farming practices, agents use the landscape as an artifact, learning to predict its productivity from a few attributes such as the area's slope and aspect. Given the dynamic nature of the landscape and its inhabitants, agents also evolve various combinations of learning strategies adapting to meet their needs. The result is the demonstration of a mechanism for incorporating artifact use learning and evolution in social simulations, leading to the emergence of favorable learning strategies