Given a set of cities with certain locations, the Traveling Salesman Problem (TSP) is to find the shortest Hamiltonian route, along which a salesman travels from a city to visit all the cities exactly once and finally returns to the starting city. The TSP is one of the most famous and well-studied NP-hard combinatorial optimization problems, which is very easy to understand but very difficult to solve optimally or near-optimally. Over the years, TSP has become a touchstone for the algorithm design. Typical methods for solving the TSP are mainly exact algorithms, approximation algorithms and heuristics. The exact algorithms may be prohibitive for large instances and the approximation algorithms may suffer from weak optimal guarantees or empirical performance (Khalil et al. 2017). Heuristics are known to be the most efficient and effective approaches for solving the TSP.

Differential evolution is a heuristic approach for the global optimisation of nonlinear and non- differentiable continuous space functions. The differential evolution algorithm belongs to a broader family of evolutionary computing algorithms. Similar to other popular direct search approaches, such as genetic algorithms and evolution strategies, the differential evolution algorithm starts with an initial population of candidate solutions. These candidate solutions are iteratively improved by introducing mutations into the population, and retaining the fittest candidate solutions that yield a lower objective function value. The differential evolution algorithm is advantageous over the aforementioned popular approaches because it can handle nonlinear and non-differentiable multi-dimensional objective functions, while requiring very few control parameters to steer the minimisation.

In the context of the introduction of intermittent renewable energies, we propose to optimize the main variables of the control rods of a nuclear power plant to improve its capability to load-follow. The design problem is a black-box combinatorial optimization problem with expensive evaluation based on a multi-physics simulator. Therefore, we use a parallel asynchronous master-worker Evolutionary Algorithm scaling up to thousand computing units. One main issue is the tuning of the algorithm parameters. A fitness landscape analysis is conducted on this expensive real-world problem to show that it would be possible to tune the mutation parameters according to the low-cost estimation of the fitness landscape features.

Agriculture is the foundation of human civilization. However, the rapid increase and aging of the global population pose challenges on this cornerstone by demanding more healthy and fresh food. Internet of Things (IoT) technology makes modern autonomous greenhouse a viable and reliable engine of food production. However, the educated and skilled labor capable of overseeing high-tech greenhouses is scarce. Artificial intelligence (AI) and cloud computing technologies are promising solutions for precision control and high-efficiency production in such controlled environments. In this paper, we propose a smart agriculture solution, namely iGrow: (1) we use IoT and cloud computing technologies to measure, collect, and manage growing data, to support iteration of our decision-making AI module, which consists of an incremental model and an optimization algorithm; (2) we propose a three-stage incremental model based on accumulating data, enabling growers/central computers to schedule control strategies conveniently and at low cost; (3) we propose a model-based iterative optimization algorithm, which can dynamically optimize the greenhouse control strategy in real-time production. In the simulated experiment, evaluation results show the accuracy of our incremental model is comparable to an advanced tomato simulator, while our optimization algorithms can beat the champion of the 2nd Autonomous Greenhouse Challenge. Compelling results from the A/B test in real greenhouses demonstrate that our solution significantly increases production (commercially sellable fruits) (+ 10.15%) and net profit (+ 87.07%) with statistical significance compared to planting experts.

The complexity of software tasks and the uncertainty of crowd developer behaviors make it challenging to plan crowdsourced software development (CSD) projects. In a competitive crowdsourcing marketplace, competition for shared worker resources from multiple simultaneously open tasks adds another layer of uncertainty to the potential outcomes of software crowdsourcing. These factors lead to the need for supporting CSD managers with automated scheduling to improve the visibility and predictability of crowdsourcing processes and outcomes. To that end, this paper proposes an evolutionary algorithm-based task scheduling method for crowdsourced software development. The proposed evolutionary scheduling method uses a multiobjective genetic algorithm to recommend an optimal task start date. The method uses three fitness functions, based on project duration, task similarity, and task failure prediction, respectively. The task failure fitness function uses a neural network to predict the probability of task failure with respect to a specific task start date. The proposed method then recommends the best tasks start dates for the project as a whole and each individual task so as to achieve the lowest project failure ratio. Experimental results on 4 projects demonstrate that the proposed method has the potential to reduce project duration by a factor of 33-78%.

In this article we present the motivation and the core thesis towards the implementation of a Quantum Knowledge Seeking Agent (QKSA). QKSA is a general reinforcement learning agent that can be used to model classical and quantum dynamics. It merges ideas from universal artificial general intelligence, constructor theory and genetic programming to build a robust and general framework for testing the capabilities of the agent in a variety of environments. It takes the artificial life (or, animat) path to artificial general intelligence where a population of intelligent agents are instantiated to explore valid ways of modelling the perceptions. The multiplicity and survivability of the agents are defined by the fitness, with respect to the explainability and predictability, of a resource-bounded computational model of the environment. This general learning approach is then employed to model the physics of an environment based on subjective observer states of the agents. A specific case of quantum process tomography as a general modelling principle is presented. The various background ideas and a baseline formalism are discussed in this article which sets the groundwork for the implementations of the QKSA that are currently in active development.

The search for binary sequences with low paper, we present a computational approach that uses peak sidelobe level value represents a formidable computational a stochastic algorithm. To locate better sequences for this our approach cannot provide optimal solutions but in a problem, we designed a stochastic algorithm that uses reasonable time we can locate optimal or near-optimal two fitness functions. Therefore, our approach is also suitable for of the autocorrelation function has a different impact on solving larger instances of the problem. It is defined with the value of the of length L in our problem is defined as follows: exponent over the autocorrelation function values. The main goal of a binary sequences problem with low peak sidelobe level is to find an optimal sequence that has the minimal PSL value, as shown in Eq. (4). 1 Introduction The binary sequences with low peak sidelobe level value S In this sequences with length L. From Eq. (1) it is evident that the number of sequences with length L is 2 The exhaustive search was also applied under the restriction The remainder of the paper is organized as follows. of m-sequence [7].

Scoring functions, which measure the plausibility of triples, have become the crux of knowledge graph embedding (KGE). Plenty of scoring functions, targeting at capturing different kinds of relations in KGs, have been designed by experts in recent years. However, as relations can exhibit intricate patterns that are hard to infer before training, none of them can consistently perform the best on existing benchmark tasks. AutoSF has shown the significance of using automated machine learning (AutoML) to design KG- dependent scoring functions. In this paper, we propose AutoSF+ as an extension of AutoSF. First, we improve the search algorithm with the evolutionary search, which can better explore the search space. Second, we evaluate AutoSF+ on the recently developed benchmark OGB. Besides, we apply AutoSF+ to the new task, i.e., entity classification, to show that it can improve the task beyond KG completion.

Population-based optimization algorithms, like evolutionary algorithms and swarm intelligence, often describe their dynamics in terms of the interplay between selective pressures and convergence. For example, strong selective pressures result in faster convergence and likely premature convergence. Weaker selective pressures may result in a slower convergence (greater computational cost) although perhaps locate a better or even global optima. An operator with a high selective pressure decreases diversity in the population more rapidly than operators with a low selective pressure, which may lead to premature convergence to suboptimal solutions. A high selective pressure limits the exploration abilities of the population.

The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the deployment of fully supervised algorithms unfeasible. On the other hand, the problem of semi-supervised large-scale data streams is little explored in the literature because most works are designed in the traditional single-node computing environments while also being fully supervised approaches. This paper offers Weakly Supervised Scalable Teacher Forcing Network (WeScatterNet) to cope with the scarcity of labelled samples and the large-scale data streams simultaneously. WeScatterNet is crafted under distributed computing platform of Apache Spark with a data-free model fusion strategy for model compression after parallel computing stage. It features an open network structure to address the global and local drift problems while integrating a data augmentation, annotation and auto-correction ($DA^3$) method for handling partially labelled data streams. The performance of WeScatterNet is numerically evaluated in the six large-scale data stream problems with only $25\%$ label proportions. It shows highly competitive performance even if compared with fully supervised learners with $100\%$ label proportions.