Competitive Coevolutionary Algorithm Research Articles

A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection

In real-world applications, feature selection is crucial for enhancing the performance of data science and machine learning models. Typically, feature selection is a complex combinatorial optimization problem and a multi-objective optimization problem. Its primary goals are to reduce the dimensionality of the dataset and enhance the performance of machine learning algorithms. The selection of features in high-dimensional datasets is challenging due to the intricate relationships between features, which pose significant challenges to the performance and computational efficiency of algorithms. This paper introduces a Knowledge-Guided Competitive Co-Evolutionary Algorithm (KCCEA) for feature selection, especially for high-dimensional features. In the proposed algorithm, we make improvements to the foundational dominance-based multi-objective evolutionary algorithm in two aspects. First, the use of feature correlation as knowledge to guide evolution enhances the search speed and quality of traditional multi-objective evolutionary algorithm solutions. Second, a dynamically allocated competitive–cooperative evolutionary mechanism is proposed, integrating the improved knowledge-guided evolution with traditional evolutionary algorithms, further enhancing the search efficiency and diversity of solutions. Through rigorous empirical testing on various datasets, the KCCEA demonstrates superior performance compared to basic multi-objective evolutionary algorithms, providing effective solutions to multi-objective feature selection problems while enhancing the interpretability and effectiveness of prediction models.

Runtime Analysis of Competitive Co-evolutionary Algorithms for Maximin Optimisation of a Bilinear Function

Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs. However, these algorithms are poorly understood and applications are often limited by pathological behaviour, such as loss of gradient, relative over-generalisation, and mediocre objective stasis. It is an open challenge to develop a theory that can predict when co-evolutionary algorithms find solutions efficiently and reliable. This paper provides a first step in developing runtime analysis for population-based competitive co-evolutionary algorithms. We provide a mathematical framework for describing and reasoning about the performance of co-evolutionary processes. To illustrate the framework, we introduce a population-based co-evolutionary algorithm called PDCoEA, and prove that it obtains a solution to a bilinear maximin optimisation problem in expected polynomial time. Finally, we describe settings where PDCoEA needs exponential time with overwhelmingly high probability to obtain a solution.

A nonlinear model and an algorithm for identifying cancer driver pathways

The identification of driver pathways has attracted considerable attention in recent years due to the high significance of driver pathways in cancer related studies. In most identification methods, the linear measurement is adopted for evaluating exclusivity, and some preset parameters are introduced to balance the contributions of different measurements. However, both the linear measurement and the preset parameters may exert negative effects on the applications of these methods. In this study, a nonlinear function is devised to measure exclusivity, and a parameter free nonlinear maximum weight submatrix (NMWS) identification model is proposed by considering coverage, exclusivity and connectivity. A competitive co-evolution algorithm (CCA) is also put forward for solving the presented NMWS model. Experimental results on both real biological data and simulated one were used compare the identification performance of the presented method with that of seven state of the art ones. The pathway detected by the presented method not only contains more genes enriched in a known signaling pathway, but also has a stronger connectivity in Protein–Protein Interaction (PPI) network. Simultaneously, the high efficiency of the presented method makes it practical in realistic applications. All of which have been confirmed through a large number of experiments.

Optimal priority assignment for real-time systems: a coevolution-based approach

In real-time systems, priorities assigned to real-time tasks determine the order of task executions, by relying on an underlying task scheduling policy. Assigning optimal priority values to tasks is critical to allow the tasks to complete their executions while maximizing safety margins from their specified deadlines. This enables real-time systems to tolerate unexpected overheads in task executions and still meet their deadlines. In practice, priority assignments result from an interactive process between the development and testing teams. In this article, we propose an automated method that aims to identify the best possible priority assignments in real-time systems, accounting for multiple objectives regarding safety margins and engineering constraints. Our approach is based on a multi-objective, competitive coevolutionary algorithm mimicking the interactive priority assignment process between the development and testing teams. We evaluate our approach by applying it to six industrial systems from different domains and several synthetic systems. The results indicate that our approach significantly outperforms both our baselines, i.e., random search and sequential search, and solutions defined by practitioners. Our approach scales to complex industrial systems as an offline analysis method that attempts to find near-optimal solutions within acceptable time, i.e., less than 16 hours.

Newton Competitive Genetic Algorithm Method for Optimization the Production of Biochemical Systems

In this work, the optimization of biochemical systems production is performed by using a hybrid method of Newton competitive genetic algorithm is presented. The proposed method works by representing the biochemical systems as a generalized mass action model, where it leads to the process of solving a complex non-linear equations system. The optimization process becomes hard and difficult when it involves multi-objective problem. This is where two objectives, namely the maximize the biochemical systems production and minimize the total amount of chemical concentrations involves. To deal with the problem, this work proposed a hybrid method of the Newton method, genetic algorithm, and competitive co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark biochemical systems and the experimental results showed that the proposed method achieved better results compared to the existing works.

Active vibration control design using the Coral Reefs Optimization with Substrate Layer algorithm

Active vibration control (AVC) via inertial-mass actuators is a viable technique to mitigate human-induced vibrations in civil structures. A multi-input multi-output (MIMO) AVC has been previously proposed in the literature to simultaneously find the sensor/actuator pairs’ optimal placements and tune the control gains. However, the method involved local gradient-based methods, which is not affordable when the number of possible locations of actuators is large. In this case, the computation time to obtain a local solution may be huge and unaffordable, which limits the number of test points and/or actuators/sensors considered. This paper proposes an alternative approach based on a recently proposed meta-heuristic, the Coral Reefs Optimization (CRO) algorithm. More concretely, an enhanced version of the CRO is considered, the Coral Reefs Optimization with Substrate Layer (CRO-SL). The CRO-SL is a competitive co-evolution algorithm in which different exploration procedures are jointly evolved within a single population of potential solutions to the problem. The proposed algorithm is thus able to promote competition among different search methods to solve hard optimization problems. In terms of structural design, this work provides an important step to improve the applicability of AVC systems to real complex structures (with a large number of vibration modes and/or with a large number of test points) by achieving global optimum designs with affordable computation time. A finite element model of a real complex floor structure is used to illustrate the contributions of this paper.

Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method

In this paper, an improve method of multi-objective optimization for biochemical system production is presented and discussed in detail. The optimization process of biochemical system production become hard and difficult when involved a large biochemical system that contain with many components. In addition, the multi-objective problem also need to be considered. Due to that, this study proposed and improve method that comprises with Newton method, differential evolution algorithm (DE) and competitive co-evolutionary algorithm(ComCA). The aim of the proposed method is to maximize the production and simultaneously minimize the total amount of chemical concentrations involves. The operation of the proposed method starts with Newton method by dealing with biochemical system production as a nonlinear equations system. Then DE and ComCA are used to represent the variables in nonlinear equation system and tune the variables in order to find the best solution. The used of DE is to maximize the production while ComCA is to minimize the total amount of chemical concentrations involves. The effectiveness of the proposed method is evaluated using two benchmark biochemical systems and the experimental results show that the proposed method perform well compared to other works.

A Quantum-Inspired Evolutionary Algorithm Using Gaussian Distribution-Based Quantization

Recent advances lead to the increase in the capability of evolutionary algorithms for tacking optimization problems. Particularly quantum-inspired computation leads to a new direction for enhancing the effectiveness of these algorithms. Existing studies on quantum-inspired algorithms focused primarily on evolving a single set of homogeneous solutions. This paper expands the scope of current research by applying quantum computing principles, in particular the quantum superposition principle proposing a new quantization approach. As a result, a quantum-inspired competitive coevolution algorithm (QCCEA) is proposed in this paper. Unlike its predecessors and traditional quantum-inspired algorithms, the proposed QCCEA uses a novel approach for quantization using Gaussian distribution which alters the selection procedure in order to identify the optimal fitness. This new approach which incorporates a new qubit architecture and processing has proved effective for numerical optimization problems as well as multiobjective problems. Experiments were performed on 20 benchmark numerical optimization problems as well as a combinatorial maze problem. The experiment results show that the proposed quantization technique has improved the performance and accuracy of the traditional non-quantized algorithms significantly for the both types of problems. This paper further discusses the impact of population before and after quantization as well as sensitivity of the parameters. The novel approach of quantization that improved the efficiency and performance is the key contribution of this work.

Structures vibration control via Tuned Mass Dampers using a co-evolution Coral Reefs Optimization algorithm

In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.

Online Discovery of Search Objectives for Test-Based Problems.

In test-based problems, commonly approached with competitive coevolutionary algorithms, the fitness of a candidate solution is determined by the outcomes of its interactions with multiple tests. Usually, fitness is a scalar aggregate of interaction outcomes, and as such imposes a complete order on the candidate solutions. However, passing different tests may require unrelated "skills," and candidate solutions may vary with respect to such capabilities. In this study, we provide theoretical evidence that scalar fitness, inherently incapable of capturing such differences, is likely to lead to premature convergence. To mitigate this problem, we propose disco, a method that automatically identifies the groups of tests for which the candidate solutions behave similarly and define the above skills. Each such group gives rise to a derived objective, and these objectives together guide the search algorithm in multi-objective fashion. When applied to several well-known test-based problems, the proposed approach significantly outperforms the conventional two-population coevolution. This opens the door to efficient and generic countermeasures to premature convergence for both coevolutionary and evolutionary algorithms applied to problems featuring aggregating fitness functions.

Competitive co‐evolutionary algorithm for constrained robust design

A competitive co-evolutionary algorithm is proposed to solve constrained robust design problems. The method uses three populations, among which one population evolves the values of the design variables and the other two evolve the values of two uncertainty variables, accounting for the target and feasibility robustness, respectively. An application to the robust design (minimisation of torque ripples) of an interior permanent magnet machine is presented. Through a worst case analysis, the result from the co-evolutionary algorithm is proven to have a more robust performance than that of the non-robust optimisation if manufacturing tolerance is taken into account. The computation time of the co-evolutionary algorithm may be significantly reduced through the use of parallel computing environments.

Virtual player design using self-learning via competitive coevolutionary algorithms

The Google Artificial Intelligence (AI) Challenge is an international contest the objective of which is to program the AI in a two-player real time strategy (RTS) game. This AI is an autonomous computer program that governs the actions that one of the two players executes during the game according to the state of play. The entries are evaluated via a competition mechanism consisting of two-player rounds where each entry is tested against others. This paper describes the use of competitive coevolutionary (CC) algorithms for the automatic generation of winning game strategies in Planet Wars, the RTS game associated with the 2010 contest. Three different versions of a prime algorithm have been tested. Their common nexus is not only the use of a Hall-of-Fame (HoF) to keep note of the winners of past coevolutions but also the employment of an archive of experienced players, termed the hall-of-celebrities (HoC), that puts pressure on the optimization process and guides the search to increase the strength of the solutions; their differences come from the periodical updating of the HoF on the basis of quality and diversity metrics. The goal is to optimize the AI by means of a self-learning process guided by coevolutionary search and competitive evaluation. An empirical study on the performance of a number of variants of the proposed algorithms is described and a statistical analysis of the results is conducted. In addition to the attainment of competitive bots we also conclude that the incorporation of the HoC inside the primary algorithm helps to reduce the effects of cycling caused by the use of HoF in CC algorithms.

QoS multicast routing protocol oriented to cognitive network using competitive coevolutionary algorithm

The human intervention in the network management and maintenance should be reduced to alleviate the ever-increasing spatial and temporal complexity. By mimicking the cognitive behaviors of human being, the cognitive network improves the scalability, self-adaptation, self-organization, and self-protection in the network. To implement the cognitive network, the cognitive behaviors for the network nodes need to be carefully designed. Quality of service (QoS) multicast is an important network problem. Therefore, it is appealing to develop an effective QoS multicast routing protocol oriented to cognitive network.In this paper, we design the cognitive behaviors summarized in the cognitive science for the network nodes. Based on the cognitive behaviors, we propose a QoS multicast routing protocol oriented to cognitive network, named as CogMRT. It is a distributed protocol where each node only maintains local information. The routing search is in a hop by hop way. Inspired by the small-world phenomenon, the cognitive behaviors help to accumulate the experiential route information. Since the QoS multicast routing is a typical combinatorial optimization problem and it is proved to be NP-Complete, we have applied the competitive coevolutionary algorithm (CCA) for the multicast tree construction. The CCA adopts novel encoding method and genetic operations which leverage the characteristics of the problem. We implement and evaluate CogMRT and other two promising alternative protocols in NS2 platform. The results show that CogMRT has remarkable advantages over the counterpart traditional protocols by exploiting the cognitive favors.

Nash Equilibrium in a two-settlement electricity market using competitive coevolutionary algorithms

This paper investigates the use of competitive coevolutionary algorithms to calculate suppliers’ optimal strategies in a deregulated electricity market. The two settlement model is used, consisting of a spot and a forward market. Agents can take part in both spot and forward transactions, and act strategically to maximise their profit from both markets. The strategic interactions of market agents are modelled as a non cooperative game. The competitive coevolutionary Algorithm is used to calculate the Nash Equilibrium strategies ensuring the best outcome for each agent. Results demonstrates the effectiveness of the coevolutionary approach to find the optimal strategies in different market situations.

Software Defect Prediction Based on Competitive Organization CoEvolutionary Algorithm

In order to improve the accuracy of prediction for software defect data sets, competitive organization coevolutionary algorithm is presented and applied for dealing with the software defect data. During this algorithm, mechanism of competition is introduced into coevolutionary algorithm. Then leagues are formed based on the importance of attributes among them. And three evolution operators which are reduced operator, allied operators and disturbed operators are developed. Furthermore, both the importance of attributes and evaluation from competition are used for the calculation of fitness function. Finally, five data sets from NASA MDP(Metrics Data Program) were used to validate the algorithm. The experimental results show that the proposed algorithm is effective for software defect prediction.

Equilibrium Calculation in Electricity Market Modeled as a Two-stage Stochastic Game using competitive Coevolutionary Algorithms

The present paper investigates the use of competitive coevolutionary algorithm in order to find supplier's optimal strategies in a deregulated electricity market, by calculating the Nash equilibrium strategies. In this work, we consider a stochastic model where spot market parameters are not known in advance. the market agents can take part in the forward and spot transactions and act strategically in order to increase their profits from both transactions.

Maintaining an Optimal Flow of Forest Products under a Carbon Market: Approximating a Pareto Set of Optimal Silvicultural Regimes for Eucalyptus fastigata

rd , 2012; revised February 24 th , 2012; accepted March 17 th , 2012 A competitive co-evolutionary Multi-Objective Genetic Algorithm (cc-MOGA) was used to approximate a Pareto front of efficient silvicultural regimes for Eucalyptus fastigata. The three objectives to be maximised included, sawlog, pulpwood and carbon sequestration payment. Three carbon price scenarios (3CPS), i.e. NZ $25, NZ $50 and NZ $100 for a tonne of CO2 sequestered, were used to assess the impact on silvicultural regimes, against a fourth non-carbon Pareto set of efficient regimes (nonCPS), determined from a cc-MOGA with two objectives, i.e. competing sawlog and pulpwood productions. Carbon prices included in stand valuation were found to influence the silvicultural regimes by increasing the rotation length and lowering the final crop number before clearfell. However, there were no significant changes in the frequency, timing, and intensity of thinning operations amongst all the four Pareto sets of solutions. However, the 3CPS were not significantly different from each other, which meant that these silvicultural regimes were insensitive to the price of carbon. This was because maximising carbon sequestration was directly related to the biological growth rate. As such an optimal mix of frequency, intensity, and timing of thinning maintained maximum growth rate for as long as possible for any one rotation.

Nash–Cournot equilibrium of a deregulated electricity market using competitive coevolutionary algorithms

In this paper, a competitive coevolutionary approach is used to find out the Nash–Cournot equilibrium of an oligopolistic electricity market based on either centralized or bilateral structure, where the transmission constraints are considered. We consider a market game played by bounded evolutionary agents, adapting their strategies to maximize their profits in a competitive environment. Under the effect of the competition, the coevolved agents are led toward an equilibrium point where each agent population is invaded by an Evolutionary stable Strategy.

Connecting Community-Grids by supporting job negotiation with coevolutionary Fuzzy-Systems

We utilize a competitive coevolutionary algorithm (CA) in order to optimize the parameter set of a Fuzzy-System for job negotiation between Community-Grids. In a Community-Grid, users are submitting jobs to their local High Performance Computing (HPC) sites over time. Now, we assume that Community-Grids are interconnected such that the exchange of jobs becomes possible: Each Community strives for minimizing the response time for their own members by trying to distribute workload to other communities in the Grid environment. For negotiation purpose, a Fuzzy-System is used to steer each site’s decisions whether to distribute or accept workload in a beneficial, yet egoistic direction. In such a system, it is essential that communities can only benefit if the workload is equitably (not necessarily equally) portioned among all participants. That is, if one community egoistically refuses to execute foreign jobs regularly, other HPC sites suffer from overloading. This, on the long run, deteriorates the opportunity to utilize them for job delegation. Thus, the egoistic community will degrade its own average performance. This scenario is particularly suited for the application of a competitive CA: the Fuzzy-Systems of the participating communities are modeled as species, which evolve in different populations while having to compete within the commonly shared ecosystem. Using real workload traces and Grid setups, we show that the opportunistic cooperation leads to significant improvements for both each community and the overall system.

Competitive co-evolutionary algorithms can solve function optimization problems

Competitive co-evolutionary algorithms (CCEAs) have many advantages, but their range of applications has been crucially limited. This study provides a simple, non-problem-specific framework to extend that range. The framework has two co-evolving populations, one of candidate solutions and one of criteria, in which these populations competitively co-evolve with each other. The framework aims to avoid candidate solutions getting stuck in a local optimum by changing the fitness landscape dynamically. Moreover, the framework has a mechanism which will establish and maintain a proper arms race. We have conducted experiments on two function optimization problems, the 1-dimensional function maximization problem and the Rastrigin function minimization problem, in order to investigate the basic properties of the framework. The results of the experiments showed that a CCEA achieves a performance which is comparable to that of a GA.