NeuroEvolution Of Augmenting Topologies Research Articles

Dynamic scheduling of hybrid flow shop problem with uncertain process time and flexible maintenance using NeuroEvolution of Augmenting Topologies

AbstractA hybrid flow shop is pivotal in modern manufacturing systems, where various emergencies and disturbances occur within the smart manufacturing context. Efficiently solving the dynamic hybrid flow shop scheduling problem (HFSP), characterised by dynamic release times, uncertain job processing times, and flexible machine maintenance has become a significant research focus. A NeuroEvolution of Augmenting Topologies (NEAT) algorithm is proposed to minimise the maximum completion time. To improve the NEAT algorithm's efficiency and effectiveness, several features were integrated: a multi‐agent system with autonomous interaction and centralised training to develop the parallel machine scheduling policy, a maintenance‐related scheduling action for optimal maintenance decision learning, and a proactive scheduling action to avoid waiting for jobs at decision moments, thereby exploring a broader solution space. The performance of the trained NEAT model was experimentally compared with the Deep Q‐Network (DQN) and five classical priority dispatching rules (PDRs) across various problem scales. The results show that the NEAT algorithm achieves better solutions and responds more quickly to dynamic changes than DQN and PDRs. Furthermore, generalisation test results demonstrate NEAT's rapid problem‐solving ability on test instances different from the training set.

Adaptation of the Neat Algorithm for Complex Problems with the Help of Quality Diversity Algorithms

The article discusses the essence of the NEAT (NeuroEvolution of Augmenting Topologies) algorithm in solving problems of neural network optimization and evolution of their topologies. An overview of the current state of use of NEAT and its adaptations in evolutionary computing research is given. The need for the Quality Diversity (QD) approach to increase the diversity and quality of solutions in complex problems is substantiated. The QD concept and its impact on the search for innovative solutions within diverse search spaces are described. The application of ViE-NEAT, which combines the advantages of survival of the fittest solutions with the principle of maintaining diversity, is described. The main aspects of ViE-NEAT are compared with the traditional NEAT, analyzing the advantages of using the survival algorithm in comparison with competitive methods. A detailed description of the MAP-Elites algorithm is given, which demonstrates an alternative approach to finding solutions by ensuring the diversity of the “illuminated” feature space, which can be integrated with NEAT to form a more diversified population of solutions. The main focus is on the methodology of integrating NEAT with MAP-Elites algorithms to create an adapted search strategy. Based on the basic principles of NEAT, the possibilities of its extension for effective solution of new problems that are not limited to traditional parametric spaces are determined. An analysis of the results demonstrating the efficiency of using the adapted NEAT algorithm in comparison with traditional approaches to the evolution of neural networks is presented.

Using a NEAT approach with curriculums for dynamic content generation in video games

This paper presents a novel exploration of the use of an evolving neural network approach to generate dynamic content for video games, specifically for a tower defence game. The objective is to employ the NeuroEvolution of Augmenting Topologies (NEAT) technique to train a NEAT neural network as a wave manager to generate enemy waves that challenge the player’s defences. The approach is extended to incorporate NEAT-generated curriculums for tower deployments to gradually increase the difficulty for the generated enemy waves, allowing the neural network to learn incrementally. The approach dynamically adapts to changes in the player’s skill level, providing a more personalised and engaging gaming experience. The quality of the machine-generated waves is evaluated through a blind A/B test with the Games Experience Questionnaire (GEQ), and results are compared with manually designed human waves. The study finds no discernible difference in the reported player experience between AI and human-designed waves. The approach can significantly reduce the time and resources required to design game content while maintaining the quality of the player experience. The approach has the potential to be applied to a range of video game genres and within the design and development process, providing a more personalised and engaging gaming experience for players.

Design and Development of Neuroevolutionary Algorithms for Cyber Security and Optimizing AI Models through Genetic Programming

Different methods have been created to make optimization processes more efficient and effective, which is a big step forward in the area of evolutionary optimization. This abstract talks about four well-known methods: Neuroevolution of Augmenting Topologies (NEAT), Genetic Algorithms (GAs), Genetic Programming (GP), and Advanced Neuroevolutionary Genetic Algorithm (ANGA). It focuses on important performance indicators like Fitness Metrics, Generalization, Efficiency and Speed, and Overall Performance. With scores of 90% in Fitness Metrics and 88% in Generalization, NEAT, a neuroevolutionary program, shows strong success in competitive tasks. With an 80% score, it does poorly in Efficiency and Speed, though. GAs are known for using a population-based method. They do very well in Efficiency and Speed (90%), but they do a little worse in Fitness Metrics and Generalization (89% and 85%, respectively). With a focus on updated computer programs, GP gets marks that are equal in Fitness Metrics, Generalization, and Efficiency and Speed (88%, 85%, and 85%, respectively). The new ANGA algorithm stands out as a top worker, doing exceptionally well in all tests. ANGA gets great marks of 93% in Fitness Metrics, 94% in Generalization, and 93% in Efficiency and Speed. This shows how well it can optimize everything. Overall Performance score of 97.78% shows how well it works as a whole, making ANGA a potential method for genetic optimization.

THE SCIENTIFIC BASIS, SOME RESULTS, AND PERSPECTIVES OF MODELING EVOLUTIONARILY CONDITIONED NOOGENESIS OF ARTIFICIAL CREATURES IN VIRTUAL BIOCENOSES

This research aimed to gain a profound understanding of virtual biocenoses intricate digital ecosystems, with the goal of elucidating and replicating the emergence and evolution of intelligence in artificial creatures – referred to as noogenesis. A comprehensive analysis of existing studies within virtual biocenoses was undertaken to glean valuable insights into the complexities of modeling dynamic ecosystems where artificial agents engaged in intricate interactions. The pivotal role of neural networks in shaping the adaptive behaviors of artificial creatures within these environments was underscored. A meticulous investigation into neural networks' evolution methodologies revealed the evolution of their architecture complexity over time, culminating in the facilitation of flexible and intelligent behaviors. However, a lack of study existed in the domain of nurturing evolutionary-based communication and cooperation capabilities within virtual biocenoses. In response to this gap, a model was introduced and substantiated through simulation experiments. The simulation results vividly illustrated the model's remarkable capacity to engender adaptive creatures endowed with the capability to efficiently respond to dynamic environmental changes. These adaptive entities displayed efficient optimization of energy consumption and resource acquisition. Moreover, they manifested both intellectual and physical transformations attributed to the evolution and encoding principles inspired by the NeuroEvolution of Augmented Topologies. Significantly, it became apparent that the evolutionary processes intrinsic to the model were inextricably linked to the environment itself, thus harmonizing seamlessly with the overarching goal of this research. Future research directions in this field were outlined. These pathways provided a foundation for further exploration into the evolution of artificial creatures in virtual biocenoses and the emergence of advanced communication and cooperation capabilities. These advancements hold the potential to move artificial life and artificial intelligence to new levels of understanding and capability.

Adaptive Neuroevolution With Genetic Operator Control and Two-Way Complexity Variation

Topology and weight evolving artificial neural network (TWEANN) algorithms optimize the structure and weights of artificial neural networks (ANNs) simultaneously. The resulting networks are typically used as policy models for solving control and reinforcement learning (RL) type problems. This paper presents a neuroevolution algorithm that aims to address the typical stagnation and sluggish convergence issues present in other neuroevolution algorithms. These issues are often caused by inadequacies in population diversity preservation, exploration/exploitation balance, and search flexibility. This new algorithm, called the Adaptive Genomic Evolution of Neural-Network Topologies (AGENT), builds on the neuroevolution of augmenting topologies (NEAT) concept. Novel mechanisms for adapting the selection and mutation operations are proposed to favorably control population diversity and exploration/exploitation balance. The former is founded on a fundamentally new way of quantifying diversity by taking a graph-theoretic perspective of the population of genomes and inter-genomic differences. Further advancements to the NEAT paradigm occur through the incorporation of variable neuronal properties and new mutation operations that uniquely allow both the growth and pruning of ANN topologies during evolution. Numerical experiments with benchmark control problems adopted from the OpenAI Gym illustrate the competitive performance of AGENT against standard RL methods and adaptive HyperNEAT, and superiority over the original NEAT algorithm. Further parametric analysis provides key insights into the impact of the new features in AGENT. This is followed by evaluation on an unmanned aerial vehicle collision avoidance problem where maneuver planning models are learnt by AGENT with 33% reward improvement over 15 generations.

Моделювання автономного проходження лабіринту з використанням алгоритму NEAT

The article deals with the problem of finding a solution for the navigational task of navigating a maze by an autonomous agent controlled by an artificial neural network (ANN). A solution to this problem was proposed by training the controlling ANN using the method of neuroevolution of augmenting topologies (NEAT). A description of the mathematical apparatus for determining the goal-oriented objective function to measure fitness of the decision-making agent, suitable for optimizing the training of ANN in the process of neuroevolution, was given. Based on the invented objective function, a software was developed to control the neuroevolutionary process using the Python programming language. A system for simulating the behavior of an autonomous robot that can navigate through a maze using input signals from various types of sensors has been created. The simulation system allows to imitate the behavior of a physical robot in a large number of experiments in a short time and with minimal expenses. The experiments performed using the created simulation system to find the optimal values of hyperparameters, which can be used for successful training of the controlling ANN by the method of neuroevolution, are presented. Additionally, the implemented new methods of visualizing the training process are described. These methods significantly simplify the search for optimal hyperparameters of the NEAT algorithm, due to the visual demonstration of the effect of changing one or another parameter on the training process.

On Neuroevolution of Multi-Input Compositional Pattern Producing Networks: A Case of Entertainment Computing, Edge Devices, and Smart Cities

This work presents a novel approach by utilizing Heterogeneous Activation Neural Networks (HA-NNs) to evolve the weights of Artificial Neural Networks (ANNs) for reinforcement learning in console and arcade computer games like Atari's Breakout and Sonic the Hedgehog. It is the first study to explore the potential of HA-NNs as potent ANNs in solving gaming-related reinforcement learning problems. Additionally, the proposed solution optimizes data transmission over networks for edge devices, marking a novel application of HA-NNs. The study achieved outstanding results, outperforming recent works in benchmark environments like CartPole-v1, Lunar Lander Continuous, and MountainCar-Continuous, with HA-NNs and ANNs evolved using the Neuroevolution of Augmenting Topologies (NEAT) algorithm. Notably, the key advancements include exceptional scores of 500 in CartPole-v1 and 98.2 in Mountain Car Continuous, demonstrating the efficacy of HA-NNs in reinforcement learning tasks. Beyond gaming, the research addresses the challenge of efficient data communication between edge devices, which has the potential to enhance performance in smart cities while reducing the load on edge devices and supporting seamless entertainment experiences with minimal commuting. This work pioneers the application of HA-NNs in reinforcement learning for computer games and introduces a novel approach for optimizing edge device communication, promising significant advancements in the fields of AI, neural networks, and smart city technologies.

MBEANN for Robotic Swarm Controller Design and the Behavior Analysis for Cooperative Transport

This study focuses on mutation-based evolving artificial neural network (MBEANN), a topology and weight evolving artificial neural network (TWEANN) algorithm. TWEANN optimizes both the connection weights and neural network structure. Primarily, MBEANN uses only mutations to evolve artificial neural networks. An individual in an MBEANN is designed to have a set of sub-networks called operons. Operons are expected to have functions during evolution because they do not recombine with other operons. In this study, we applied MBEANN to design a controller for a robotic swarm on cooperative transport, where the following canonical evolving artificial neural network (EANN) methods do not work well. For comparison with MBEANN, we used an EANN with a fixed network structure and neuroevolution of augmenting topologies (NEAT), which is a widely used TWEANN algorithm. We confirmed that the robot controller that evolved with the MBEANN outperformed the structure-fixed EANN and NEAT controllers. In addition, we investigated the behavior of the swarm robot obtained using the proposed method, in which we deactivated each operon to extract its function. The results show that operons could have their functions, and that several operons could strengthen one another’s functions.

Hybrid self-attention NEAT: a novel evolutionary self-attention approach to improve the NEAT algorithm in high dimensional inputs

This article presents a "Hybrid Self-Attention NEAT" method to improve the original NeuroEvolution of Augmenting Topologies (NEAT) algorithm in high-dimensional inputs. Although the NEAT algorithm has shown a significant result in different challenging tasks, as input representations are high dimensional, it cannot create a well-tuned network. Our study addresses this limitation by using self-attention as an indirect encoding method to select the most important parts of the input. In addition, we improve its overall performance with the help of a hybrid method to evolve the final network weights. The main conclusion is that Hybrid Self- Attention NEAT can eliminate the restriction of the original NEAT. The results indicate that in comparison with evolutionary algorithms, our model can get comparable scores in Atari games with raw pixels input with a much lower number of parameters.

Mechanical Characterization of Coarse-Grained Waste Rocks Using Large-Scale Triaxial Tests and Neuroevolution of Augmenting Topologies

Mining operations produce large quantities of waste rocks, which are usually disposed of in waste rock piles, but can also be valorized in mine haul roads. The engineering performance of these haul roads significantly depends on the mechanical characteristics of the materials used for the construction. However, available experimental studies on coarse-grained waste rocks are relatively limited, mainly because of their large grain size and the scarcity of adapted testing equipment. In this study, a series of repeated load and monotonic triaxial tests (specimens 300 mm in diameter and 600 mm in height) were carried out to evaluate the resilient modulus, permanent deformation, and shear strength of coarse-grained waste rocks (up to 60 mm in diameter) with different gradations. Results showed that an increasing in maximum particle size and compaction effort resulted in a larger resilient modulus and shear strength and smaller permanent deformation. The optimal gravel-to-sand ratio to maximize resilient modulus and shear strength was around 5. Permanent strain was relatively constant when the gravel-to-sand ratio was between 1 and 5, but it decreased significantly when the ratio increased to 8. The impact of fines content and water content on the mechanical properties was relatively limited. Also, the MR-θ model and Rahman and Erlingsson model showed good fitting performance for resilient modulus and permanent strain, respectively. Finally, neuroevolution of augmenting topologies (NEAT) was used to develop a machine learning model for predicting the resilient modulus of waste rocks, based on 265 data sets. The model showed reliable accuracy, simple topology, and high generalizability capacity. The findings described in this article should be beneficial for the valorization of waste rocks in pavement engineering.

An Insight into NeuroEvolution and Genetic Algorithms for Text Classification

Natural Language Processing (NLP) systems have, over the past decade, shifted from using rule-based techniques to using machine learning-based algorithms. This has led to the development of different architectures and models for different tasks. Some of these architectures include models like the transformers, the CNN and the RNN, which have now become ubiquitous in NLP. However, designing these neural network architectures usually requires in-depth analysis and knowledge of multiple domain areas involved with the problem at hand. In our work, we evaluate an alternative solution to this problem in the domain of text classification. Here, we suggest using the Genetic Algorithm with gradient descent (GAGD) and NeuroEvolution of Augmenting Topologies (NEAT) to search for an optimal neural architecture for the Reuters-21578 and 20 Newsgroups datasets. We evaluate and compare the results of the two algorithms against the current state-of-the-art architectures and provide insight into their performance.

Drifting Streaming Peaks-Over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast

This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generation data into ramp and non-ramp datasets, where time-varying dynamics are taken into account by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events. We then train different neural networks based on each dataset to learn the different dynamics of wind farm generation by the NeuroEvolution of Augmenting Topologies (NEAT), which can obtain the best network topology and weighting parameters. As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Based on the developed self-evolving neural networks, both distributional and point forecasts are developed. The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The experiment results indicate that the accuracy improvement in a 60 min horizon forecast in terms of the mean absolute error (MAE) is at least 33.6% for the whole year data and at least 37% for the ramp events. Moreover, the distributional forecast in terms of the continuous rank probability score (CRPS) is improved by at least 35.8% for the whole year data and at least 35.2% for the ramp events.

Class binarization to neuroevolution for multiclass classification

Multiclass classification is a fundamental and challenging task in machine learning. The existing techniques of multiclass classification can be categorized as (1) decomposition into binary (2) extension from binary and (3) hierarchical classification. Decomposing multiclass classification into a set of binary classifications that can be efficiently solved by using binary classifiers, called class binarization, which is a popular technique for multiclass classification. Neuroevolution, a general and powerful technique for evolving the structure and weights of neural networks, has been successfully applied to binary classification. In this paper, we apply class binarization techniques to a neuroevolution algorithm, NeuroEvolution of Augmenting Topologies (NEAT), that are used to generate neural networks for multiclass classification. We propose a new method that applies Error-Correcting Output Codes (ECOC) to design the class binarization strategies on the neuroevolution for multiclass classification. The ECOC strategies are compared with the class binarization strategies of One-vs-One and One-vs-All on three well-known datasets of Digit, Satellite, and Ecoli. We analyse their performance from four aspects of multiclass classification degradation, accuracy, evolutionary efficiency, and robustness. The results show that the NEAT with ECOC performs high accuracy with low variance. Specifically, it shows significant benefits in a flexible number of binary classifiers and strong robustness.

Evolving Deep Neural Networks with Cultural Algorithms for Real-Time Industrial Applications

The goal of this paper is to investigate the applicability of evolutionary algorithms to the design of real-time industrial controllers. Present-day “deep learning” (DL) is firmly established as a useful tool for addressing many practical problems. This has spurred the development of neural architecture search (NAS) methods in order to automate the model search activity. CATNeuro is a NAS algorithm based on the graph evolution concept devised by Neuroevolution of Augmenting Topologies (NEAT) but propelled by cultural algorithm (CA) as the evolutionary driver. The CA is a network-based, stochastic optimization framework inspired by problem solving in human cultures. Knowledge distribution (KD) across the network of graph models is a key to problem solving success in CAT systems. Two alternative mechanisms for KD across the network are employed. One supports cooperation (CATNeuro) in the network and the other competition (WM). To test the viability of each configuration prior to use in the industrial setting, they were applied to the design of a real-time controller for a two-dimensional fighting game. While both were able to beat the AI program that came with the fighting game, the cooperative method performed statistically better. As a result, it was used to track the motion of a trailer (in lateral and vertical directions) using a camera mounted on the tractor vehicle towing the trailer. In this second real-time application (trailer motion), the CATNeuro configuration was compared to the original NEAT (elitist) method of evolution. CATNeuro is found to perform statistically better than NEAT in many aspects of the design including model training loss, model parameter size, and overall model structure consistency. In both scenarios, the performance improvements were attributed to the increased model diversity due to the interaction of CA knowledge sources both cooperatively and competitively.

Evolving and training of Neural Network to Play DAMA Board Game Using NEAT Algorithm

Neuroevolutionary algorithms, such as NeuroEvolution of Augmenting Topologies (NEAT) in Machine Learning (ML) methods, are utilized for training and playing computer games due to increased research in the Artificial Intelligence (AI) field. NEAT is a genetic algorithm for the generation of evolving artificial neural networks. In this paper, a new study is presented. A Dama board game is designed, and the NEAT algorithm is implemented to develop and train the populations of neural networks for playing the game efficiently. Different inputs and outputs for the network are used, and various network sizes are tried for the game to reach or pass the human level. This paper aims to make a neural network that plays a Dama game-like human or close to them by training different neural networks for many generations. The experimental results show that neural networks have been trained for several thousands of generations, and they have played more than one million games. It is concluded that using more input to handle information is better for the learning process. It is also found out that a set of values for NEAT parameters is good for big neural networks like those used in this paper.

Recurrent NEAT Assisted 2D-DOA Estimation With Reduced Complexity for Satellite Communication Systems

Direction-of-arrival (DOA) estimation plays a vital role in the field of array signal processing. However, the need for heavy computing tasks in most traditional DOA algorithms, e.g., multiple signal classification (MUSIC), makes their engineering practicality significantly compromised in satellite communication systems. The neuroevolution of augmenting topologies (NEAT) can quickly search for appropriate topologies and weights of neural network functions, but its computational complexity is still too high for satellite systems. This paper proposes a modified NEAT architecture featuring a recurrent structure (RNEAT) that only needs a small number of phase components of the received signal covariance matrix as inputs to reduce the complexity and simplify the neural network architecture. The proposed RNEAT incorporated with multiple signal classification (RNEAT-MUSIC) features low complexity to achieve high resolution and low complexity simultaneously. Validation has been done by applying the proposed method in a two-dimensional direction of arrival estimation (2D-DOA) problem. Results show that the proposed RNEAT-MUSIC efficiently restricts the scanning region before forwarding the covariance matrix to the MUSIC stage. Consequently, the computational workload is reduced by 3/4 compared with the traditional 2D-MUSIC algorithm while maintaining satisfactory DOA resolution.

Neuroevolution of Augmenting Topologies for Artificial Evolution: A Case Study of Kinesis of Creature in the Various Mediums

The motivation of the present study is to evolve virtual creatures in a diverse simulated 3D environment. The proposed scheme is based on the artificial evolution using the Neuro Evolution of Augmenting Topologies (NEAT) algorithm to educe a neural network that controls the muscle forces of the artificial creatures. The morphologies of the creatures are established using the Genetic Algorithm (GA) method based on the distance metrics fitness function. The concept of damaging crossover of neural networks and genetic language for the morphology of creatures has been considered in the morphologies of the artificial creature. Creatures with certain morphological traits consume a large time to optimize their kinetics, thus they are placed in a separate species to limit the search. The simulation results in the significant kinetics of artificial creatures (2-5 limbs) in virtual mediums with varying dynamic and static coefficients of friction (0.0-4.0). The motion of artificial creatures in the simulated medium was determined at different angles and demonstrated in the 3D space.

UAV-based coffee yield prediction utilizing feature selection and deep learning

Unmanned Aerial Vehicles (UAVs) combined with machine learning have a great potential for crop yield estimation. In this study, a UAV equipped with an RGB (Red, Green, Blue) camera and computer vision algorithms were used to estimate coffee tree height and crown diameter, and for the prediction of coffee yield. Data were collected for 144 trees between June 2017 and May 2018, in the Minas Gerais, Brazil. Six parameters (leaf area index - LAI, tree height, crown diameter, and the individual RGB band values) were used to develop UAV-based yield prediction models. First, a feature ranking was performed to identify the most significant parameter(s) and month(s) for data collection and yield prediction. Based on the feature rankings, the LAI and the crown diameter were determined as the most important parameters. Five algorithms were used to develop yield prediction models: (i) linear support vector machines (SVM), (ii) gradient boosting regression (GBR), (iii) random forest regression (RFR), (iv) partial least square regression (PLSR), and (v) neuroevolution of augmenting topologies (NEAT). The mean absolute percentage error (MAPE) was used to evaluate the yield prediction models. The best result was obtained by the NEAT algorithm (MAPE of 31.75%) for a reduced dataset containing only the most important features (LAI and the crown diameter) and the most important months (December 2017 and April 2018). The results suggest that a dataset of the most important month (December) could be used for the yield prediction model, reducing the need for extensive data collection (e.g., monthly data collection).

Neuroevolutionary Control for Autonomous Soaring

The energy efficiency and flight endurance of small unmanned aerial vehicles (SUAVs) can be improved through the implementation of autonomous soaring strategies. Biologically inspired flight techniques such as dynamic and thermal soaring offer significant energy savings through the exploitation of naturally occurring wind phenomena for thrustless flight. Recent interest in the application of artificial intelligence algorithms for autonomous soaring has been motivated by the pursuit of instilling generalized behavior in control systems, centered around the use of neural networks. However, the topology of such networks is usually predetermined, restricting the search space of potential solutions, while often resulting in complex neural networks that can pose implementation challenges for the limited hardware onboard small-scale autonomous vehicles. In exploring a novel method of generating neurocontrollers, this paper presents a neural network-based soaring strategy to extend flight times and advance the potential operational capability of SUAVs. In this study, the Neuroevolution of Augmenting Topologies (NEAT) algorithm is used to train efficient and effective neurocontrollers that can control a simulated aircraft along sustained dynamic and thermal soaring trajectories. The proposed approach evolves interpretable neural networks in a way that preserves simplicity while maximizing performance without requiring extensive training datasets. As a result, the combined trajectory planning and aircraft control strategy is suitable for real-time implementation on SUAV platforms.