This introduction is intended for everyone, specially those who are interested in learning about something new. No pre-existing knowledge of the subject or any scientific background is expected.

Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatiotemporal inputs. This paper presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm. There are several unique features in the proposed architecture that tightly link with the HTM algorithm. A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed. The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM. The crossbar for the SP avoids dark spots caused by unutilized crossbar regions and supports rapid on-chip training within 2 clock cycles. This research also leverages plasticity mechanisms such as neurogenesis and homeostatic intrinsic plasticity to strengthen the robustness and performance of the SP. The proposed design is benchmarked for image recognition tasks using MNIST and Yale faces datasets, and is evaluated using different metrics including entropy, sparseness, and noise robustness. Detailed power analysis at different stages of the SP operations is performed to demonstrate the suitability for mobile platforms.

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years since it is matched with the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms existing methods in terms of solution quality and improves best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to analysis further their sensitivity to the performance of our method.

Designers increasingly leverage autonomous software tools that make decisions independent of the designer. Examples abound in virtually every design field. For example, semiconductor chip designers use tools that make decisions about placement and logic checking. Game designers rely on software that generates initial drafts of virtual worlds. Autonomous tools employ artificial intelligence methods, including machine learning, pattern recognition, meta-heuristics, and evolutionary algorithms to generate design artifacts beyond any human's capabilities. A naïve view suggests these tools will someday replace human designers in the design process. An alternative perspective is that humans will continue to play an important role but also that this role is changing.

In multi-objective Bayesian optimization and surrogate-based evolutionary algorithms, Expected HyperVolume Improvement (EHVI) is widely used as the acquisition function to guide the search approaching the Pareto front. This paper focuses on the exact calculation of EHVI given a nondominated set, for which the existing exact algorithms are complex and can be inefficient for problems with more than three objectives. Integrating with different decomposition algorithms, we propose a new method for calculating the integral in each decomposed high-dimensional box in constant time. We develop three new exact EHVI calculation algorithms based on three region decomposition methods. The first grid-based algorithm has a complexity of $O(m\cdot n^m)$ with $n$ denoting the size of the nondominated set and $m$ the number of objectives. The Walking Fish Group (WFG)-based algorithm has a worst-case complexity of $O(m\cdot 2^n)$ but has a better average performance. These two can be applied for problems with any $m$. The third CLM-based algorithm is only for $m=3$ and asymptotically optimal with complexity $\Theta(n\log{n})$. Performance comparison results show that all our three algorithms are at least twice faster than the state-of-the-art algorithms with the same decomposition methods. When $m>3$, our WFG-based algorithm can be over $10^2$ faster than the corresponding existing algorithms. Our algorithm is demonstrated in an example involving efficient multi-objective material design with Bayesian optimization.

Out of practical concerns and with the expectation to achieve high overall efficiency of the resource utilization, this paper transforms the large scale black-box optimization problems with limited resources into online decision problems from the perspective of dynamic multi-armed bandits, a simplified view of Markov decision processes. The proposed Online Decisioning Meta-heuristic framework (ODM) is particularly well suited for real-world applications, with flexible compatibility for various kinds of costs, interfaces for easy heuristic articulation as well as fewer hyper-parameters for less variance in performance. Experimental results on benchmark functions suggest that ODM has demonstrated significant capabilities for online decisioning. Furthermore, when ODM is articulated with three heuristics, competitive performance can be achieved on benchmark problems with search dimensions up to 10000.

Students are usually allocated tosupervisors for their projects by means of a centralized human decision maker or by means of interactions between students and staff members. The decision makers have to take into consideration the preferences of both students and supervisors with respect to the conduct of the project, as well as departmental constraintssuch as minimum and maximum levels of workload (in terms of supervision) for each supervisor. This situation results in an extremely time consuming process, and a suboptimal allocation due to a large and complex search space faced by human decision makers. Automating this process by applying artificial intelligence techniques may enhance the process in terms of satisfaction and performance of students with these individual projects. In this article, we present a genetic algorithm for matching students to supervisors accordingto both students' and supervisors' preferences and the constraints of the department. The rationale behind this problem is matching an appropriate student with a supervisor for the development of an individual project.The problem of matching students to supervisors, or students to projects [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], is a subclass of the wider problem of matching between two sets, one of the most studied fields in computer sciencedue to its applications to a wide range of domains such as the hospital/residents (HR) or the college admission (CA) problem [14, 15, 16]. Particularly, the student-supervisor allocation problem solved in this article can be considered as an instance of the CA problem with lower and upper quotas, where the colleges are the supervisors, both colleges and students (i.e., supervisors andstudents in our case) have some representation of preferences on each other for the conduct of a project, and the minimum and maximum quotas are the minimum and maximum number of students to be supervised by staff members.

ULTI-task learning[3], [23] is a subfield of machine learning, particularly transfer learning [17], [22], [24], [25], which uses auxiliary data or knowledge from related/similar tasks to facilitate the learning in a new task. As a result, a learning model for the new task can be built with much less task-specific training data. Or, in other words, with the same amount of task-specific data, a much better model could be trained. In multi-task learning, multiple related learning tasks are performed simultaneously using a (partially) shared model representation. As a result, the common information contained in these related tasks can be exploited to improve the learning efficiency and generalization performance of each task-specific model. Multi-task optimization (MTO) [6], [12], [16], [19] applies multi-task learning to optimization to study how to effectively and efficiently tackle multiple optimization problems simultaneously. Evolutionarymulti-tasking [16], or multifactorial optimization (MFO)[12], is an emerging subfield of MTO, which integrates evolutionary computation and multi-task learning.

Hyper-heuristics are a novel tool. They deal with complex optimization problems where standalone solvers exhibit varied performance. Among such a tool reside selection hyper-heuristics. By combining the strengths of each solver, this kind of hyper-heuristic offers a more robust tool. However, their effectiveness is highly dependent on the 'features' used to link them with the problem that is being solved. Aiming at enhancing selection hyper-heuristics, in this paper we propose two types of transformation: explicit and implicit. The first one directly changes the distribution of critical points within the feature domain while using a Euclidean distance to measure proximity. The second one operates indirectly by preserving the distribution of critical points but changing the distance metric through a kernel function. We focus on analyzing the effect of each kind of transformation, and of their combinations. We test our ideas in the domain of constraint satisfaction problems because of their popularity and many practical applications. In this work, we compare the performance of our proposals against those of previously published data. Furthermore, we expand on previous research by increasing the number of analyzed features. We found that, by incorporating transformations into the model of selection hyper-heuristics, overall performance can be improved, yielding more stable results. However, combining implicit and explicit transformations was not as fruitful. Additionally, we ran some confirmatory tests on the domain of knapsack problems. Again, we observed improved stability, leading to the generation of hyper-heuristics whose profit had a standard deviation between 20% and 30% smaller.

Natural evolution gives the impression of leading to an open-ended process of increasing diversity and complexity. If our goal is to produce such open-endedness artificially, this suggests an approach driven by evolutionary metaphor. On the other hand, techniques from machine learning and artificial intelligence are often considered too narrow to provide the sort of exploratory dynamics associated with evolution. In this paper, we hope to bridge that gap by reviewing common barriers to open-endedness in the evolution-inspired approach and how they are dealt with in the evolutionary case - collapse of diversity, saturation of complexity, and failure to form new kinds of individuality. We then show how these problems map onto similar issues in the machine learning approach, and discuss how the same insights and solutions which alleviated those barriers in evolutionary approaches can be ported over. At the same time, the form these issues take in the machine learning formulation suggests new ways to analyze and resolve barriers to open-endedness. Ultimately, we hope to inspire researchers to be able to interchangeably use evolutionary and gradient-descent-based machine learning methods to approach the design and creation of open-ended systems.