https://www.cse.unr.edu/~bebis/boosting.pdf

Object detection using feature subset selection

This paper presents a new approach to acquiring and using problem specific knowledge during a genetic algorithm (GA) search. A GA augmented with a case-based memory of past problem solving attempts learns to obtain better performance over time on sets of similar problems. Rather than starting anew on each problem, we periodically inject a GA's population with appropriate intermediate solutions to similar previously solved problems. Perhaps, counterintuitively, simply injecting solutions to previously solved problems does not produce very good results. We provide a framework for evaluating this GA-based machine-learning system and show experimental results on a set of design and optimization problems. These results demonstrate the performance gains from our approach and indicate that our system learns to take less time to provide quality solutions to a new problem as it gains experience from solving other similar problems in design and optimization.

Learning with case-injected genetic algorithms

In this paper, we approach the problem of constructing ensembles of classifiers from the point of view of instance selection. Instance selection is aimed at obtaining a subset of the instances available for training capable of achieving, at least, the same performance as the whole training set. In this way, instance selection algorithms try to keep the performance of the classifiers while reducing the number of instances in the training set. Meanwhile, boosting methods construct an ensemble of classifiers iteratively focusing each new member on the most difficult instances by means of a biased distribution of the training instances. In this work, we show how these two methodologies can be combined advantageously. We can use instance selection algorithms for boosting using as objective to optimize the training error weighted by the biased distribution of the instances given by the boosting method. Our method can be considered as boosting by instance selection. Instance selection has mostly been developed and used for k -nearest neighbor (k -NN) classifiers. So, as a first step, our methodology is suited to construct ensembles of k -NN classifiers. Constructing ensembles of classifiers by means of instance selection has the important feature of reducing the space complexity of the final ensemble as only a subset of the instances is selected for each classifier. However, the methodology is not restricted to k-NN classifier. Other classifiers, such as decision trees and support vector machines (SVMs), may also benefit from a smaller training set, as they produce simpler classifiers if an instance selection algorithm is performed before training. In the experimental section, we show that the proposed approach is able to produce better and simpler ensembles than random subspace method (RSM) method for k-NN and standard ensemble methods for C4.5 and SVMs.

Constructing Ensembles of Classifiers by Means of Weighted Instance Selection

https://sci2s.ugr.es/keel/pdf/keel/articulo/NN-Garcia05.pdf

An alternative approach for neural network evolution with a genetic algorithm: Crossover by combinatorial optimization

This paper presents a cooperative evolutionary approach for the problem of instance selection for instance based learning. The model presented takes advantage of one of the recent paradigms in the field of evolutionary computation: cooperative coevolution. This paradigm is based on a similar approach to the philosophy of divide and conquer. In our method, the training set is divided into several subsets that are searched independently. A population of global solutions relates the search in different subsets and keeps track of the best combinations obtained. The proposed model has the advantage over standard methods in that it does not rely on any specific distance metric or classifier algorithm. Additionally, the fitness function of the individuals considers both storage requirements and classification accuracy, and the user can balance both objectives depending on his/her specific needs, assigning different weights to each one of these two terms. The method also shows good scalability when applied to large datasets.

The proposed model is favorably compared with some of the most successful standard algorithms, IB3, ICF and DROP3, with a genetic algorithm using CHC method, and with four recent methods of instance selection, MSS, entropy-based instance selection, IMOEA and LVQPRU. The comparison shows a clear advantage of the proposed algorithm in terms of storage requirements, and is, at least, as good as any of the other methods in terms of testing error. A large set of 50 problems from the UCI Machine Learning Repository is used for the comparison. Additionally, a study of the effect of instance label noise is carried out, showing the robustness of the proposed algorithm.

The major contribution of our work is showing that cooperative coevolution can be used to tackle large problems taking advantage of its inherently modular nature. We show that a combination of cooperative coevolution together with the principle of divide-and-conquer can be very effective both in terms of improving performance and in reducing computational cost.

/pdf/a-cooperative-coevolutionary-algorithm-for-instance-24wtnank21.pdf

A cooperative coevolutionary algorithm for instance selection for instance-based learning

We use a genetic algorithm augmented with a long term memory to design control strategies for a simulated robot, a mobile vehicle operating in a two-dimensional environment. The simulated robot has five touch sensors, two sound sensors, and two motors that drive locomotive tank tracks. A genetic algorithm trains the robot in several specially-designed simulation environments for evolving basic behaviors such as food approach, obstacle avoidance, and wall following. Control strategies for a more complex environment are then designed by selecting solutions from the stored strategies evolved for basic behaviors, ranking them according to their performance in the new complex environment and introducing them into a genetic algorithm's initial population. This augmented memory-based genetic algorithm quickly combines the basic behaviors and finds control strategies for performing well in the more complex environment.

Combining Control Strategies Using Genetic Algorithms with Memory

Predefined-time robust control with formation constraints and saturated controls

Maintaining the given operational area is critical in guaranteeing the safety of nonlinear second-order multiple autonomous agents. The properties of multiagent systems and several physical constraints, including bounded modeling error and actuator saturation, dramatically affect the maneuverability of multiagent systems inside the specified operational area. Moreover, the existing safety control algorithms heavily rely on the boundaries of the operational area. To overcome this issue, by constructing a novel scalable control technique, the safety area for multiagent systems can be transformed into input-constrained control barriers along each coordinate of motion for agents. It is shown that the safety of each agent and the global asymptotic stability are guaranteed under the proposed distributed control algorithm. The asymmetrical closed-form scheme for the agent's safety rule is built by applying the adjustable low and high bounds of the control signals associated with the actual control inputs, which are repeatedly computed by using new local measurements as the agents move, and the saturated control inputs with asymmetrical constraints are ensured. The absolute values of the modeling errors and external disturbances can be tracked by the proposed safety controller. Super-twisting control (STC) is employed to address the formation constraint problem of multiagent systems, where the effect that arises from uncertain nonlinear complexity of the agents and external disturbances is eliminated. Moreover, finite-time convergence, a desirable robust behavior of multiagent systems, and the formation constraint are simultaneously achieved. Furthermore, the stability of the proposed integrated control strategy for multiagent systems is analyzed, which reveals that the proposed distributed safety control can seamlessly integrate with the robust control protocol with minimum modification under the directed information interaction topology. Safety formation control calibration and tuning are carried out, and comparative simulation results are provided to illustrate the effective performance of the obtained theoretical results. 

Safety control using barrier certificates for multiagent systems with input saturation and formation constraints

https://link.springer.com/content/pdf/10.1007/s11071-022-07843-3.pdf

Correction to: Predefined-time robust control with formation constraints and saturated controls

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Combining Control Strategies Using Genetic Algorithms with Memory

Predefined-time robust control with formation constraints and saturated controls

Safety control using barrier certificates for multiagent systems with input saturation and formation constraints

Correction to: Predefined-time robust control with formation constraints and saturated controls