The Planning and Scheduling (PS) problem plays a vital role in several domains, such as economics, military, management, finance, and games, where finding the optimal plan and schedule to achieve specific goals is essential.In this article, we present a Genetic Algorithm for the Planning and Scheduling (GAPS) problem in the StarCraft II Build Order Optimization problem (SC2 BO) context -as it signifies that modern strategy games present a more challenging environment than classical planning problems.We evaluate the performance of GAPS and compare it with state-of-the-art methods.Experimental results provide valuable insight into the effectiveness of GA in the context of the PS Problem under various configurations, notably in the context of Lamarckianism and the Baldwin Effect.Ultimately, this research enhances the understanding of GA application for the PS problem, offering notable insights regarding GA performance and potential for future work.

This paper presents an improved Gap-based Memetic Differential Evolution (GaMeDE2), the modification of the GaMeDE method, which took second place in the GECCO 2020 Competition on Niching Methods for Multimodal Optimization.GaMeDE2 has reduced complexity, fewer parameters, redefined initialization, selection operator, and removed processing phases.The method is verified using standard benchmark function sets (classic ones and CEC2013) and a newly proposed benchmark set comprised of deceptive functions.A detailed comparison to state-of-the-art methods (like HVCMO and SDLCSDE) is presented, where the proposed GaMeDE2 outperforms or gives similar results to other methods.The document is concluded by discussing various insights on the problem instances and the methods created as a part of the research.

In the recent years, it has been shown that real world-problems are often comprised of two, interdependent subproblems.Often, solving them independently does not lead to the solution to the entire problem.In this article, a Travelling Thief Problem is considered, which combines a Travelling Salesman Problem with a Knapsack Problem.A Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is investigated, along with its recent modification -a Non-Dominated Tournament Genetic Algorithm (NTGA).Each method is investigated in two configurations.One, with generic representation, and genetic operators.The other, specialized to the given problem, to show how the specialization of genetic operators leads to better results.The impact of the modifications introduced by NTGA is verified.A set of Quality Measures is used to verify the convergence, and diversity of the resulting PF approximations, and efficiency of the method.A set of experiments is carried out.It is shown that both methods work almost the same when generic representation is used.However, NTGA outperforms classical NSGA-II in the specialized results.

A Travelling Salesman Problem (TSP) is an NPhard combinatorial problem that is very important for many real-world applications.In this paper, it is shown, that proposed approach solves multi-objective TSP (mTSP) more effectively than other investigated methods, i.e.Non-dominated Sorting Genetic Algorithm II (NSGA-II).The proposed methods use rank and crowding distance (well-known from NSGA-II), combining those mechanisms in a novel, unique way: competing and coevolving in the evolution process.The proposed modifications are investigated and verified by the benchmark mTSP instances, and results are compared to other methods.

This paper presents a software library as a research and educational tool for Multi-Skill Resource-Constrained Scheduling Problem. The following useful tools have been implemented in Java: instance Generator, solution validator, solution visualizer and example solvers: Greedy algorithm and Genetic Algorithm. All tools are supported by iMOPSE dataset which consists of 36 instances and additional ’small’ 6 instances for educational purpose. In the paper, three test studies are described: (1) educational use of 6 ’small’ instances, (2) optimization of cost or duration of a schedule, and (3) simple bicritieria optimization of cost/duration of a final schedule. All described tools/examples are freely published on iMOPSE homepage.

This paper presents methods solving MS-RCPSP as a main task-resource-time assignment optimization problem.In the paper there are presented four variants of Evolutionary Algorithm applied to MS-RCPSP problem: concerning prioritization the tasks (or resources), combined task-resources prioritizing approach and co-evolution based approach that effectively solves problem dividing it to two subproblems.All approaches are examined using benchmark MS-RCPSP iMOPSE dataset and results show that the problem decomposition is effective.All experiments are described, statistically verified and summarized.Conclusions and promising areas of future work are presented.

This paper presents multiple variances of selection operator used in Non-dominated Sorting Genetic Algorithm II applied to solving Bi-Objective Multi-Skill Resource Constrained Project Scheduling Problem.A hybrid Differential Evolution with Greedy Algorithm has been proven to work very well on the researched problem and so it is used to probe the multiobjective solution space.It is then determined whether a multiobjective approach can outperform single-objective approaches in finding potential Pareto Fronts.Additional modified selection operators and a clone prevention method have been introduced and experiments have shown the increase in efficiency caused by their utilization.

In this paper, we define a new practical technology-driven Resource Constrained Scheduling Problem (t-RCPSP).We propose three approaches, applying constructive heuristics to tackle effectively the practical application of RCPSP.In the RCPSP formulation, the constraints are defined to design the tasks in the spaces constructed by non-and renewable resources, without violating the precedence relationships and technologies in real world problem that exists in Plastic and Rubber Processing company.The difficulty of t-RCPSP is NP-hard and we proposed three constructive specialized methods: duration based heuristics (DBH), locally optimal resource usage PEC and NEH heuristic adaptation.The paper presents results of computational experiments that show the effectiveness of the proposed approaches.

In this paper novel project scheduling difficulty estimations are proposed for Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP).The main goal of introducing the complexity estimations is an attempt of estimation the project complexity before launching the optimization process.What is more, the dataset instance generator is also presented as a tool to create new instances for extending the research area.Furthermore, the dataset proposed in previous works is extended by new instances, described thoroughly and released as a benchmark dataset.The dataset instances are also scheduled using simple heuristic and greedy algorithm in duration-and cost-oriented optimization modes.Finally, a brief summary of investigated methods and potential further research directions is presented.

In this paper, hybrid ant colony optimization (HAntCO) approach in solving multi-skill resource-constrained project scheduling problem (MS-RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with ant colony optimization (ACO). Furthermore, a novel approach for updating pheromone value has been proposed based on both the best and worst solutions stored by ants. The objective of this paper is to research the usability and robustness of ACO and its hybrids with priority rules in solving MS-RCPSP. Experiments have been performed using artificially created dataset instances based on real-world ones. We published those instances that can be used as a benchmark. Presented results show that ACO-based hybrid method is an efficient approach. More directed search process by hybrids makes this approach more stable and provides mostly better results than classical ACO.

This paper presents a new approach to the graph coloring problem (GCP) which utilizes information about conflict localization in a given coloring. In this context a partial fitness function (pff) and its usage to specialize genetic operators and phenotypic measure of diversity in population are described. Particular attention is given to the investigation of the influence of the population size and the usage of genetic operators on the character of the evolution, especially influence leading to a premature convergence in the evolution process. Experiments based on benchmark DIMACS graphs are presented.