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The number of research works on dynamic constrained optimization problems has been increasing rapidly over the past two decades. In this domain, many real-life decision problems need to be solved repeatedly with changing data and parameters. However, no research on dynamic problems with changes in the coefficients of the constraint functions has been reported. In this paper, to deal with such problems, a new evolutionary framework with multiple novel mechanisms is proposed. The new mechanisms are for (1) dealing with both linear and non-linear components in the constraint functions, (2) identifying the rate of change in the coefficients of the variables and (3) updating the population efficiently after every change occurs in the problem. To evaluate the performance of the proposed algorithm, we designed a new set of 13 dynamic benchmark problems, each of which consists of 20 dynamic changes and 3 different scenarios. The results demonstrate that the proposed algorithm significantly contributes in achieving good quality solutions, high feasibility rates and fast convergence in rapidly changing environments. In addition, the framework shows its capability of using different meta-heuristics to solve dynamic problems.

The Resource-Constrained Project Scheduling Problem (RCPSP) is a challenging optimization problem. In RCPSPs, it is very common to consider homogeneous activities, which means all activities require all types of resources. In practice, the activities are often singular because they usually require one single resource to execute an activity. The existing algorithms may be used for solving this variant of RCPSPs with a simple modification. However, they are computationally expensive due to unnecessary resource constraints. In this paper, we propose a customised evolutionary algorithm integrated with three heuristics for the singular activities. The first heuristic is based on the earliest start time with an aim to rectify an infeasible schedule. The second heuristic is based on neighbourhood swapping which is used to find the best possible alternatives. The third heuristic is used to further enhance the quality of the schedule. The performance of the proposed framework has been tested by solving a wide range of benchmark problems and the obtained results revealed that the proposed approach outperformed the existing algorithms. In addition, statistical and parametric testing show the value and characteristics of the proposed approach.

Real-time strategy (RTS) games are a subgenre of strategy video games. Due to their importance in practical decision-making and digital entertainment over the last two decades, many researchers have explored different algorithmic approaches for controlling agents within RTS games and learning effective strategies and tactics. Among the techniques, coevolutionary algorithms proved to be one of the most popular and successful algorithms for developing such games, in which players can compete or cooperate to achieve the given game’s mission. However, as many alternative designs exist with their analysis and the applications reported in diverse publications, a review covering the evolution of such algorithms would be valuable for researchers and practitioners in this domain. This paper aims to provide a systematic review by highlighting why and how coevolution is used in RTS games and analysis of the recent work. The review conducted follows procedural steps to identify, filter, analyse and discuss the existing literature. This structured review articulates the purposes of using coevolution in RTS games and highlights several open questions for future research in this domain.

In dynamic constrained optimization, changes may occur in either the objective function or constraint functions, or both. However, although research on dynamic optimization has been growing significantly, it is centered mainly around unconstrained problems. On the other hand, research on dynamic constrained problems has considered simple extensions of those conducted on unconstrained ones. These approaches are usually computationally expensive and exhibit slow convergence in changed environments. To develop an effective algorithm for dynamic constrained problems with changes occurring in only the constraint's space, in this research, we propose a sensitive constraint detection mechanism that provides valuable information for determining the movements of solutions in changing environments. As, by incorporating it with a search process, the convergence of an algorithm can be accelerated, it is adopted with a multi-operator evolutionary type of algorithm which is then tested on a set of constrained dynamic problems. The experimental results clearly demonstrate the benefits of this proposed approach.

Resource-constrained project scheduling is one of the most widely studied research problems. Although a large number of algorithms have been developed for solving these problems, many of them ignored several practical issues such as resource unavailability and disruptions, and a recovery plan for after a disruption. This paper investigates different cost-effective measures for project scheduling problems under resource disruptions, in which the disrupted resources are dynamically recovered as the project progresses. Firstly, a new proactive scheduling technique is proposed to determine the make-span with an emphasis on maximizing the floating resources that can be used as a buffer to handle any future disruption effectively. Secondly, we introduce a bi-objective approach for reactive scheduling if a disruption occurs, in which both the revised make-span and recovery cost are minimized. Both proactive and reactive models are solved using a specially designed multi-method based evolutionary optimization algorithm, with the results obtained showing the benefits of the proposed method in comparison to state-of-the-art algorithms.

The performance of a Convolutional Neural Network (CNN) highly depends on its architecture and corresponding parameters. Manually designing a CNN is a time-consuming process in regards to the various layers that it can have, and the variety of parameters that must be set up. Increasing the complexity of the network structure by employing various types of connections makes designing a network even more challenging. Evolutionary computation as an optimisation technique can be applied to arrange the CNN layers and/or initiate its parameters automatically or semi-automatically. Dense network and Residual network are two popular network structures that were introduced to facilitate the training of deep networks. In this paper, leveraging the potentials of Dense and Residual blocks, and using the capability of evolutionary computation, we propose an automatic evolutionary model to detect an optimum and accurate network structure and its parameters for medical image segmentation. The proposed evolutionary DenseRes model is employed for segmentation of six publicly available MRI and CT medical datasets. The proposed model obtained high accuracy while employing networks with minimal parameters for the segmentation of medical images and outperformed manual and automatic designed networks, including U-Net, Residual U-Net, Dense U-Net, Non-Bypass Dense, NAS U-Net, AdaresU-Net, and EvoU-Net.

In many real-world decision problems, there are specific objective, decision variables, conditions, data and/or parameters that may vary over time. When these problems are solved through an optimization process, they are generally known as dynamic optimization. Dynamic optimization is a challenging research topic as the optimal solution usually moves with any change in the problem environment. In locating the optimal point in any optimization problems, the effectiveness of the search process is influenced by the nature of the problem landscape. In order to improve the effectiveness of the search process, in this article, a new approach is developed by integrating a landscape-based strategy with appropriately designed evolutionary algorithms for solving dynamic problems. The proposed approach is named as Landscape Influenced Dynamic Optimization Algorithm (LIDOA). LIDOA checks the similarity before and after the changed environment which is then used as an input to guide the evolutionary search process. The dynamic benchmark functions from IEEE-CEC2009 are solved using LIDOA. LIDOA was able to enhance the performance of the evolutionary algorithms in their adaptation to dynamic changes, which in turn enhanced their ability to attain better results.

PyDDRBG is a Python framework for generating tunable test problems for static and dynamic multimodal optimization. It allows for quick and simple generation of a set of predefined problems for non-experienced users, as well as highly customized problems for more experienced users. It easily integrates with an arbitrary optimization method. It can calculate the optimization performance when measured according to the robust mean peak ratio. PyDDRBG is expected to advance the fields of static and dynamic multimodal optimization by providing a common platform to facilitate the numerical analysis, evaluation, and comparison in these fields.

Term weighting is one of the most commonly used approaches, which works by assigning weights to terms, that aims to improve the performance of information retrieval or text categorization tasks. In this paper, we present a novel term weighting technique, called discriminative feature spamming technique (DFST), which identifies distinctive terms, based on a term utility criteria (TUC), and then spams them to increase their discriminative power. The experimental results show that the DFST outperformed a set of time-tested term weighting schemes, from the information retrieval field. All the experiments were performed on the largest ever Roman Urdu (RU) dataset of 11000 reviews, which was collected and annotated for this work. In addition, a custom tokenizer was built, which further improved classification accuracy. A cross-scheme comparison was performed, which showed that the results obtained by using the newly proposed DFST, were statistically significant and better than previous approaches.