Mini-Batching, Gradient-Clipping, First-versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression'

The aim of Symbolic Regression (SR) is to discover interpretable expressions that accurately describe data. The accuracy of an expression depends on both its structure and coefficients. To keep the structure simple enough to be interpretable, effective coefficient optimisation becomes key. Gradient-based optimisation is clearly effective at...

Evolving-to-Learn with Spiking Neural Networks

Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning...

Developing Equations for Free Vibration Parameters of Bistable Composite Plates Using Multi-Objective Genetic Programming

For the last three decades, bistable composite laminates have gained publicity because of their outstanding features, including having two stable shapes and the ability to change these states. A common challenge regarding the analysis of these structures is the high computational cost of existing analytical methods to estimate their natural...

NeuroSCA: Evolving Activation Functions for Side-Channel Analysis

The choice of activation functions can significantly impact the performance of neural networks. Due to an ever-increasing number of new activation functions being proposed in the literature, selecting the appropriate activation function becomes even more difficult. Consequently, many researchers approach this problem from a different angle, in...

Evolvability degeneration in multi-objective genetic programming for symbolic regression

Genetic programming (GP) is one of the best approaches today to discover symbolic regression models. To find models that trade off accuracy and complexity, the non-dominated sorting genetic algorithm II (NSGA-II) is widely used. Unfortunately, it has been shown that NSGA-II can be inefficient: in early generations, low-complexity models over...

RACE:GP – a Generic Approach to Automatically Creating and Evaluating Hybrid Recommender Systems

In recent years, recommender systems have become a fundamental part of our online experience. Users rely on such systems in situations with many potential choices, such as watching a movie on a streaming service, reading a blog post, or listening to a song. Traditionally, these systems use techniques such as collaborative filtering and content...

Symbolic Regression Methods for Reinforcement Learning

Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as neural networks or basis...

Formal synthesis of analytic controllers: An evolutionary approach

Control design for modern safety-critical cyber-physical systems still requires significant expert-knowledge, since for general hybrid systems with temporal logic specifications there are no constructive methods. Nevertheless, in recent years multiple approaches have been proposed to automatically synthesize correct-by-construction controllers....

Design and Application of Gene-pool Optimal Mixing Evolutionary Algorithms for Genetic Programming

Machine learning is impacting modern society at large, thanks to its increasing potential to effciently and effectively model complex and heterogeneous phenomena. While machine learning models can achieve very accurate predictions in many applications, they are not infallible. In some cases, machine learning models can deliver unreasonable...

Intent-Based Networking with Programmable Data Planes

Switches that can be (re)programmed through the network programming language P4 are able to completely change – even while in the field – the way they process packets. While powerful, P4 code is inherently static, as it is written and installed to accommodate a particular network requirement. Writing new P4 code each time new requirements arise...

Accurate prediction of liquid-solid fluidized bed porosity in drinking water treatment processes using empirical data-driven genetic programming models

For an accurate prediction of the porosity of a liquid-solid homogenous fluidized bed, various empirical prediction models have been developed. Symbolic regression machine learning techniques are suitable for analyzing experimental fluidization data to produce empirical expressions for porosity as a function not only of fluid velocity and...

Optimal control via reinforcement learning with symbolic policy approximation

Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper addresses the problem of finding a smooth policy...

A genetic tango attack against the David–Prasad RFID ultra-lightweight authentication protocol

Radio frequency identification (RFID) is a powerful technology that enables wireless information storage and control in an economical way. These properties have generated a wide range of applications in different areas. Due to economic and technological constrains, RFID devices are seriously limited, having small or even tiny computational...

Automatic Discovery of Distributed Algorithms for Large-Scale Systems

In recent years, large-scale systems have become mainstream at a very high pace. Typical examples of large-scale systems are MANETs, Wireless Sensor Networks, Pervasive Computing, Swarm Robotics, etc. These systems distinguish them- selves by the large number of devices they embody, and emergent behaviors they exhibit: Behavior that is globally...

Bing Forecast

Bing Technology, a Philadelphia, USA based software company, seeks to develop a software framework that can be used to create forecasts for a wide range of predictive domains. In particular, they would like to create an application of this framework that is able to perform stock market forecasting. The goal of our project is to develop an...

Genetic Programming as an approach to the problem of synthesis

Many problems do not have a direct solution in the form of a known algorithm or program to solve such a problem. These problems include, for example, the designing of electrical circuits and producing robots capable of locomotion. These are all part of a greater problem: the problem of synthesis. How can you make a computer design circuits and...