It is a known fact that the performance of optimization algorithms for NP-Hard problems vary from instance to instance. We observed the same trend when we comprehensively studied multi-objective evolutionary algorithms (MOEAs) on a six benchmark instances of discrete time-cost trade-off problem (DTCTP) in a construction project. In this paper, instead of using a single algorithm to solve DTCTP, we use a portfolio approach that takes multiple algorithms as its constituent. We proposed portfolio comprising of four MOEAs, Non-dominated Sorting Genetic Algorithm II (NSGA-II), the strength Pareto Evolutionary Algorithm II (SPEA-II), Pareto archive evolutionary strategy (PAES) and Niched Pareto Genetic Algorithm II (NPGA-II) to solve DTCTP. The result shows that the portfolio approach is computationally fast and qualitatively superior to its constituent algorithms for all benchmark instances. Moreover, portfolio approach provides an insight in selecting the best algorithm for all benchmark instances of DTCTP.

Few days back, I started working on a practice problem – Big Mart Sales. After applying some simple models and doing some feature engineering, I landed up on 219th position on the leader board. Not bad – but I needed something better. So, I started searching for optimization techniques which could improve my score. It was during this search that I was introduced to genetic algorithms. After applying Genetric algorithm to the practice problem, I ended up taking a considerable leap on the leaderboard.

University of Pennsylvania 3700 Hamilton Walk Philadelphia, PA 19104 lacava@upenn.edu Recently we proposed a general, ensemble-based feature engineering wrapper (FEW) that was paired with a number of machine learning methods to solve regression problems. Here, we adapt FEW for supervised classification and perform a thorough analysis of fitness and survival methods within this framework. Our tests demonstrate that two fitness metrics, one introduced as an adaptation of the silhouette score, outperform the more commonly used Fisher criterion. We analyze survival methods and demonstrate that ϵ-lexicase survival works best across our test problems, followed by random survival which outperforms both tournament and deterministic crowding. We conduct a benchmark comparison to several classification methods using a large set of problems and show that FEW can improve the best classifier performance in several cases. We show that FEW generates consistent, meaningful features for a biomedical problem with different ML pairings.

The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites. The ability of SAIL to efficiently produce both accurate models and diverse high performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.

This work studies how an AI-controlled dog-fighting agent with tunable decision-making parameters can learn to optimize performance against an intelligent adversary, as measured by a stochastic objective function evaluated on simulated combat engagements. Gaussian process Bayesian optimization (GPBO) techniques are developed to automatically learn global Gaussian Process (GP) surrogate models, which provide statistical performance predictions in both explored and unexplored areas of the parameter space. This allows a learning engine to sample full-combat simulations at parameter values that are most likely to optimize performance and also provide highly informative data points for improving future predictions. However, standard GPBO methods do not provide a reliable surrogate model for the highly volatile objective functions found in aerial combat, and thus do not reliably identify global maxima. These issues are addressed by novel Repeat Sampling (RS) and Hybrid Repeat/Multi-point Sampling (HRMS) techniques. Simulation studies show that HRMS improves the accuracy of GP surrogate models, allowing AI decision-makers to more accurately predict performance and efficiently tune parameters.

In my previous article, I have explained the basics about Genetic Algorithms. After it was published, I got many requests to discuss more about the Fitness Function and Evaluation Strategies. In this article, we will discuss about fitness functions and how to come up with a fitness function for a given problem. Fitness Function (also known as the Evaluation Function) evaluates how close a given solution is to the optimum solution of the desired problem. It determines how fit a solution is.

This is the first part or a series of posts. To have a short introduction, read my Intro post. It is not rare for technology and engineering to take inspiration from nature's great designs. In this post, I will talk about genetic or evolutionary algorithms, their role in robotics, and more widely in computer science. Evolutionary algorithms are inspired by the natural process of evolution and natural selection.

Some time ago I published a blog post with the title Know your data structures!. In this previous post I explained how I improved the running time of a genetic algorithm. The post Learning Club 16: Genetic Algorithms appeared first on verenahaunschmid.

Scientists have been studying exoskeletons in nature for years, and they've been trying to figure out how to adapt them for human use. After all, a powered exoskeleton could change the lives of people who have mobility issues, whether due to age, injury or disease. The problem is that exoskeletons aren't one size fits all. Adapting them to individual humans is a difficult and time-consuming process. But now, researchers at Carnegie Mellon University may have found a way to make it a whole lot easier.

Artificial Intelligence (AI) is everywhere these days, but it's rarely discussed in detail, or with specific examples of how and why it will help improve the way we do things. Let's address that shortcoming by digging into a particular variant of AI that holds great promise: Swarm Intelligence. Swarm Intelligence is the idea of using many simplistic machine learning models each good at one small task to solve bigger, more complex problems. The idea is analogous to how swarms or hives act in the natural world. Take ants, for example: each performs a simple task that helps that hive work as a complex system.