Most of the real world optimization problems in different domains demonstrate dynamic behavior, which can be in the form of changes in the objective function, problem parameters and/or constraints for different time p...
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ISBN:
(纸本)9781479945153
Most of the real world optimization problems in different domains demonstrate dynamic behavior, which can be in the form of changes in the objective function, problem parameters and/or constraints for different time periods. Detecting the points in time where a change occurs in the landscape is a critical issue for a large number of evolutionary dynamic optimization techniques in the literature. In this paper, we present an empirical study whose focus is the performance evaluation of various sensor-based detection schemes by using two well known dynamic optimization problems, which are moving peaks benchmark (MPB) and dynamic knapsack problem (DKP). Our experimental evaluation by using two dynamic optimization problem validates the sensor-based detection schemes considered, where the effectiveness of each scheme is measured with the average rate of correctly identified changes and the average number of sensors invoked to detect a change.
This work is the first attempt to investigate the neural dynamics of a simulated robotic agent engaged in minimally cognitive tasks by employing evolved instances of the Kuramoto model of coupled oscillators as its ne...
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ISBN:
(纸本)9781424481262
This work is the first attempt to investigate the neural dynamics of a simulated robotic agent engaged in minimally cognitive tasks by employing evolved instances of the Kuramoto model of coupled oscillators as its nervous system. The main objectives are to shed new light into the role of neuronal synchronisation and phase towards the generation of cognitive behaviours and to initiate an investigation on the efficacy of such systems as practical robot controllers. The first experiment is an active categorical perception task in which the robot has to discriminate between moving circles and squares. In the second task, the robotic agent has to approach moving circles with both normal and inverted vision thus adapting to both scenarios. These tasks were chosen for being considered as benchmarks in the evolutionary robotics and adaptive behaviour communities. The results obtained indicate the feasibility of the framework in the analysis and generation of embodied cognitive behaviours.
The Team Orienteering Problem (namely TOP) consists in finding the routings for a set of vehicles that maximize the total profit reached by visiting a series of customers. In this paper, a hybrid multi-objective evolu...
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ISBN:
(纸本)9781509064656
The Team Orienteering Problem (namely TOP) consists in finding the routings for a set of vehicles that maximize the total profit reached by visiting a series of customers. In this paper, a hybrid multi-objective evolutionary algorithm based on a special genetic algorithm and local search operators is proposed for approximately solving the TOP. Two conflicting objectives are considered: to minimize the total travel cost and to maximize the profit linked to the visited customers. The performance of the proposed method is evaluated on a set of benchmark instances extracted from Chao et al. [2] and its provided results are compared to those reached by the best methods available in the literature. Encouraging results have been obtained.
This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modellin...
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ISBN:
(数字)9783030437220
ISBN:
(纸本)9783030437213;9783030437220
This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.
This paper proposes evolutionary trajectory optimization methodology for suborbital spaceplanes that combines direct and inverse trajectory design approach. Guidance of suborbital spaceplane has attracted attention as...
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This paper proposes evolutionary trajectory optimization methodology for suborbital spaceplanes that combines direct and inverse trajectory design approach. Guidance of suborbital spaceplane has attracted attention as a method to generate and update new trajectories in flight, and trajectory optimization by evolutionary computation enables global search. The trajectory of a spaceplane is subject to several strict constraints including flight path, aerodynamic loads, and longitudinal trim, making it difficult to find feasible solutions. The proposed methodology focuses on the dynamics of the trajectory, and flight constraints are handled efficiently. In the region where the angle of attack constraint changes drastically from transonic to hypersonic due to longitudinal trim constraints, the time history control input is directly designed to obtain the trajectory. In the end of the flight after deceleration to subsonic speed, the inverse designmethod is applied to satisfy the path constraints. The proposed method was validated by simulation, and it was confirmed that a diversity of solutions that fully satisfy the constraints can be obtained with a relatively small number of generations and individuals.
The complexity of distributed computing systems and their increasing interaction with the physical world impose challenging requirements in terms of adaptation, robustness, and resilience to attack. Based on their rel...
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ISBN:
(纸本)9781424448906
The complexity of distributed computing systems and their increasing interaction with the physical world impose challenging requirements in terms of adaptation, robustness, and resilience to attack. Based on their reliance on heuristics, algorithms for consensus, where members of a group agree on a course of action, are particularly sensitive to these conditions. Given the ability of natural organisms to respond to adversity, many researchers have investigated biologically-inspired approaches to designing robust distributed systems. In this paper, we describe a study in the use of digital evolution, a type of artificial life system, to produce a distributed behavior for reaching consensus. The evolved algorithm employs a novel mechanism for probabilistically reaching consensus based on the frequency of messaging. Moreover, this design approach enables us to change parameters based on the specifics of the desired system, with evolution producing corresponding flavors of consensus algorithms. Our results demonstrate that artificial life systems can be used to discover solutions to engineering problems, and that experiments in artificial life can inspire new studies in distributed protocol development.
A self-organizing swarm of autonomous unmanned areal vehicles (UAVs) can provide a quick response to cyber-attacks in austere civilian and military environments. However, if a UAV swarm relies on centralized control, ...
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ISBN:
(纸本)9781728124872
A self-organizing swarm of autonomous unmanned areal vehicles (UAVs) can provide a quick response to cyber-attacks in austere civilian and military environments. However, if a UAV swarm relies on centralized control, synchronization of swarm members, pre-planned actions or rule-based systems, it may not provide adequate and timely response against cyber attacks. We introduce game theory (GT) and biology inspired flight control algorithms to be run by each autonomous UAV to detect, localize and counteract rogue electromagnetic signal emitters. Each UAV positions itself such that the swarm tracks mobile adversaries while maintaining uniform node distribution and connectivity of the mobile ad-hoc network (MANET). UAVs use only their respective local neighbor information to determine their individual actions. Simulation experiments in OPNET show that our algorithms can provide an adequate area coverage over mobile interference sources. Our solution can be employed for civilian and military applications that require agile responses in dynamic environments.
The associative classification field includes really interesting approaches for building reliable classifiers and any of these approaches generally work on four different phases (data discretization, pattern mining, r...
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ISBN:
(纸本)9789897583698
The associative classification field includes really interesting approaches for building reliable classifiers and any of these approaches generally work on four different phases (data discretization, pattern mining, rule mining, and classifier building). This number of phases is a handicap when big datasets are analysed. The aim of this work is to propose a novel evolutionary algorithm for efficiently building associative classifiers in Big Data. The proposed model works in only two phases (a grammar-guided genetic programming framework is performed in each phase): 1) mining reliable association rules;2) building an accurate classifier by ranking and combining the previously mined rules. The proposal has been implemented on Apache Spark to take advantage of the distributed computing. The experimental analysis was performend on 40 well-known datasets and considering 13 algorithms taken from literature. A series of non-parametric tests has also been carried out to determine statistical differences. Results are quite promising in terms of reliability and efficiency on high-dimensional data.
This paper summarizes the keynote I gave on the SEAMS 2020 conference. Noting the power of natural evolution that makes living systems extremely adaptive, I describe how artificial evolution can be employed to solve d...
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ISBN:
(纸本)9781450379625
This paper summarizes the keynote I gave on the SEAMS 2020 conference. Noting the power of natural evolution that makes living systems extremely adaptive, I describe how artificial evolution can be employed to solve design and optimization problems in software. Thereafter, I discuss the Evolution of Things, that is, the possibility of evolving physical artefacts and zoom in on a (r)evolutionary way of creating 'bodies' and 'brains' of robots for engineering and fundamental research.
This paper is focused on a comparative analysis of the performance of two master-slave parallelization methods, the basic generational scheme and the steady-state asynchronous scheme. Both can be used to improve the c...
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ISBN:
(纸本)9783642386091
This paper is focused on a comparative analysis of the performance of two master-slave parallelization methods, the basic generational scheme and the steady-state asynchronous scheme. Both can be used to improve the convergence speed of multi-objective evolutionary algorithms (MOEAs) that rely on time-intensive fitness evaluation functions. The importance of this work stems from the fact that a correct choice for one or the other parallelization method can lead to considerable speed improvements with regards to the overall duration of the optimization. Our main aim is to provide practitioners of MOEAs with a simple but effective method of deciding which master-slave parallelization option is better when dealing with a time-constrained optimization process.
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