Bio: Ahmed Gad received his B.Sc. degree with excellent with honors in information technology from the Faculty of Computers and Information (FCI), Menoufia University, Egypt, in July 2015. For being ranked first in his faculty, he was recommended to work as a teaching assistant in one of the Egyptian institutes in 2015 and then in 2016 to work as a teaching assistant and a researcher in his faculty. His current research interests include deep learning, machine learning, artificial intelligence, digital signal processing, and computer vision.

DEPSO [1], or called DEPS, is an algorithm for (constrained) numerical optimization problem (NOP). DEPSO combines the advantages of Particle Swarm Optimization (PSO) and Differential Evolution (DE). It is incorporated into cooperative group optimization (CGO) system [2]. The DEPSO paper has been cited over 400 times with various applications. DEPSO was also implemented (by Sun Microsystems Inc.) into NLPSolver (Solver for Nonlinear Programming), an extension of Calc in Apache OpenOffice.

We propose practical extensions to Bayesian optimization for solving dynamic problems. We model dynamic objective functions using spatiotemporal Gaussian process priors which capture all the instances of the functions over time. Our extensions to Bayesian optimization use the information learnt from this model to guide the tracking of a temporally evolving minimum. By exploiting temporal correlations, the proposed method also determines when to make evaluations, how fast to make those evaluations, and it induces an appropriate budget of steps based on the available information. Lastly, we evaluate our technique on synthetic and real-world problems.

Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. Suppose that a data scientist has an image dataset divided into a number of classes and an image classifier is to be created. After the data scientist investigated the dataset, the K-nearest neighbor (KNN) seems to be a good option.

He started his talk with a broader philosophical statement: "Anything is possible." Referring to SpaceX and Tesla as the most cutting edge companies he has funded, he continued, "The future of technology that we couldn't imagine 15–20 years ago is obviously now possible. There is no doubt that the future of cars will be electric and autonomous. He then reflected on the SpaceX story. "I remember Elon came back from Russia very disappointed with the realization that there is a very weird supply chain in this industry.

An Atari-playing artificial intelligence created by researchers at the University of Freiburg in Germany has discovered a never-before-seen bug in the classic game Qbert. Using an inexplicable and seemingly random series of moves, the algorithm achieved an unprecedented high score in a matter of minutes. The researchers explained how they trained their A.I. to achieve an impossible result rivaling James T. Kirk's defeat of the Kobayashi Maru in a paper posted on the preprint side arXiv on February 24. Rather than employing a standard reinforcement learning approach, they used a lesser-known technique called evolutionary strategy. As the name suggests, the method is loosely based of the Darwinian concept of natural selection.

A new Atari-playing AI appears to use some underhanded tricks to get high scores when left to its own devices. What's new: An AI that learns through a trial-and-error technique called evolution strategies has been pitted against eight Atari games. Its approach gradually mutates the way it tackles tasks, keeping hold of the successful tricks and discarding ones that don't work. Any means necessary: But New Scientist notes that when playing the arcade classic Q*bert, the AI developed some unusual winning strategies. It found a software bug that it could exploit to get points, and a trick where carefully planned suicide allowed it to progress through the game. Why it matters: On one hand, it shows how evolutionary approaches let AI succeed without any human help.

We introduce an automatic machine learning (AutoML) modeling architecture called Autostacker, which combines an innovative hierarchical stacking architecture and an Evolutionary Algorithm (EA) to perform efficient parameter search. Neither prior domain knowledge about the data nor feature preprocessing is needed. Using EA, Autostacker quickly evolves candidate pipelines with high predictive accuracy. These pipelines can be used as is or as a starting point for human experts to build on. Autostacker finds innovative combinations and structures of machine learning models, rather than selecting a single model and optimizing its hyperparameters. Compared with other AutoML systems on fifteen datasets, Autostacker achieves state-of-art or competitive performance both in terms of test accuracy and time cost.

The design of gaits for robot locomotion can be a daunting process which requires significant expert knowledge and engineering. This process is even more challenging for robots that do not have an accurate physical model, such as compliant or micro-scale robots. Data-driven gait optimization provides an automated alternative to analytical gait design. In this paper, we propose a novel approach to efficiently learn a wide range of locomotion tasks with walking robots. This approach formalizes locomotion as a contextual policy search task to collect data, and subsequently uses that data to learn multi-objective locomotion primitives that can be used for planning. As a proof-of-concept we consider a simulated hexapod modeled after a recently developed microrobot, and we thoroughly evaluate the performance of this microrobot on different tasks and gaits. Our results validate the proposed controller and learning scheme on single and multi-objective locomotion tasks. Moreover, the experimental simulations show that without any prior knowledge about the robot used (e.g., dynamics model), our approach is capable of learning locomotion primitives within 250 trials and subsequently using them to successfully navigate through a maze.

The well-known Influence Maximization (IM) problem has been actively studied by researchers over the past decade, with emphasis on marketing and social networks. Existing research have obtained solutions to the IM problem by obtaining the influence spread and utilizing the property of submodularity. This paper is based on a novel approach to the IM problem geared towards optimizing clicks and consequently revenue within anOnline Social Network (OSN). Our approach diverts from existing approaches by adopting a novel, decision-making perspective through implementing Stochastic Dynamic Programming (SDP). Thus, we define a new problem Influence Maximization-Revenue Optimization (IM-RO) and propose SDP as a method in which this problem can be solved. The SDP method has lucrative gains for an advertiser in terms of optimizing clicks and generating revenue however, one drawback to the method is its associated "curse of dimensionality" particularly for problems involving a large state space. Thus, we introduce the Lawrence Degree Heuristic (LDH), Adaptive Hill-Climbing (AHC) and Multistage Particle Swarm Optimization (MPSO) heuristics as methods which are orders of magnitude faster than the SDP method whilst achieving near-optimal results. Through a comparative analysis on various synthetic and real-world networks we present the AHC and LDH as heuristics well suited to to the IM-RO problem in terms of their accuracy, running times and scalability under ideal model parameters. In this paper we present a compelling survey on the SDP method as a practical and lucrative method for spreading information and optimizing revenue within the context of OSNs.