Lamarckian Inheritance Improves Robot Evolution in Dynamic Environments

Temporal evolution of the dynamical environment around asteroid (101955) Bennu

With the rapid advancement of robotics and artificial intelligence, embodied intelligent agents operating in dynamic and unstructured environments have emerged as a key technological direction within digital twin systems. Nevertheless, their exploration capabilities in indoor digital-twin scenes remain limited. Dense semantic maps incur substantial maintenance costs and exhibit poor adaptability to dynamic environments. Furthermore, current navigation processes lack mechanisms for assessing and revisiting semantic relevance, leading to redundant exploration and inefficient resource utilization. Finally, environmental changes often result in semantic anchor degradation, with existing methods lacking effective incremental update and self-evolution mechanisms to maintain long-term consistency. To address these challenges, this paper proposes a multimodal, lightweight mapping, and revisitable self-evolving exploration agent (LRSA) for indoor digital-twin scenarios. The framework consists of three key modules: a multi-scale structured environment modeling process based on sparse semantic mapping, a large-model-driven memory-intelligence system that maintains an “area-object-feature” relational network, and a perceptual-memory self-evolution mechanism that marks inactive objects upon navigation failure and uses a memory large language model (MemoLLM) to generate and iteratively refine candidate search strategies for dynamic memory updating. Experiments conducted on the HM3D indoor dataset and the TurtleBot3 platform demonstrate that LRSA achieves a Success Rate (SR) of 55.2% and a Success weighted by Path Length (SPL) of 30.5%, surpassing state-of-the-art baselines such as ZSON, PSL, and PixNav. The proposed framework eliminates reliance on dense maps, enabling lightweight sparse modeling with reduced computational cost, synergistic memory-intelligence semantic reasoning, and self-adaptive evolution in dynamic environments. Its contributions enhance the robustness and efficiency of embodied agents’ open-world navigation and provide real-time, long-term semantic updating for urban digital twin systems, supporting autonomous “self-perception, self-evolution, and self-maintenance.”

This paper presents the evolutionary programming with an ensemble of memories to deal with optimization problems in dynamic environments. The proposed algorithm modifies a recent version of evolutionary programming by introducing a simulated-annealing-like dynamic strategy parameter as well as applying local search towards the most improving directions. Diversity of the population is enhanced by an ensemble of external archives that serve as short-term and long-term memories. The archive members also act as the basic solutions when environmental changes occur. The algorithm is tested on a set of 6 multimodal problems with a total 49 change instances provided by CEC 2009 Competition on Evolutionary Computation in Dynamic and Uncertain Environments and the results are presented.

A prediction mechanism for Memetic Algorithm is presented in this paper. The Predictive Memetic Algorithm (PMA) uses a nonlinear regression method to estimate the parameters used by the algorithm to obtain good solutions in a dynamic and stochastic environment. The algorithm is applied to nonlinear data sets and performance is compared with genetic and simulated annealing algorithms. When compared with the existing methods, the proposed method generates a relatively small error difference after prediction thereby proving its superior performance. A dynamic stochastic environment is used for experimentation, so as to determine the efficacy of the algorithm on non-stationary problem environments.

Predictive analytics including statistical techniques, predictive modelling, machine learning, and data mining that analyse current and historical facts to make predictions about future or otherwise unknown events. Higher education institutions nowadays are under increasing pressure to respond to national and global economic, political and social changes such as the growing need to increase the proportion of students in certain disciplines, embedding workplace graduate attributes and ensuring that the quality of learning programs are both nationally and globally relevant. However, in higher education institution, there are significant numbers of students that stop their studies before graduation, especially for undergraduate students. Problem related to stopping out student and late or not graduating student can be improved by applying analytics. Using analytics, administrators, instructors and student can predict what will happen in future. Administrator and instructors can decide suitable intervention programs for at-risk students and before students decide to leave their study. Many different machine learning techniques have been implemented for predictive modelling in the past including decision tree, k-nearest neighbour, random forest, neural network, support vector machine, naïve Bayesian and a few others. A few attempts have been made to use Bayesian network and dynamic Bayesian network as modelling techniques for predicting at- risk student but a few challenges need to be resolved. The motivation for using dynamic Bayesian network is that it is robust to incomplete data and it provides opportunities for handling changing and dynamic environment. The trends and directions of research on prediction and identifying at-risk student are developing prediction model that can provide as early as possible alert to administrators, predictive model that handle dynamic and changing environment and the model that provide real-time prediction.

This paper presents a computational methodology based on Genetic Algorithms with Genotype Editing (GAE) for investigating the role of RNA editing in dynamic environments. This model is constructed based on several genetic editing characteristics that are gleaned from the RNA editing system as observed in several organisms. We have previously expanded the traditional Genetic Algorithm (GA) with artificial editing mechanisms (Rocha, 1995, 1997), and studied the benefits of including straightforward Genotype Editing in GA for several machine learning problems (Huang and Rocha, 2003, 2004). We show that the incorporation of genotype editing provides a means for artificial agents with genetic descriptions to gain greater phenotypic plasticity. Artificial agents use genotype edition to their advantage by linking it to environmental context. The ability to link changes in the environment with editing parameters gives organisms an adaptive advantage as genotype expression can become contextually regulated. The study of this RNA editing model in changing environments has shed some light into the evolutionary implications of RNA editing. We expect that our methodology will both facilitate determining the evolutionary role of RNA editing in biology, and advance the current state of research in Evolutionary Computation and Artificial Life.

Adaptation and Evolution in Dynamic Persistent Environments

Adaptive team performance is essential in high-pressure, unpredictable environments. This study investigates the impact of team coordination and team communication on adaptive team performance. Data were collected from 230 team members using structured questionnaires and analyzed through Partial Least Squares (PLS). Results reveal significant and positive relationships between coordination and communication, and adaptive team performance. These findings highlight the importance of strengthening team functional processes to enhance responsiveness and resilience. The study provides practical insights for enhancing team effectiveness in dynamic operational environments, particularly where rapid decision-making and flexibility are essential for success in unpredictable and challenging conditions.

When a robot manipulator performs tasks in a dynamic unstructured environment, it not only should account for where and how it should move, but also ensure that the robot can effectively avoid collisions with static and moving obstacles. Planning in absolutely unstructured and dynamic environment is much more challenging, up-to-date there is no universal solution to this problem. In this paper, we believed the concept that for motion planning in real dynamic environments, usually there is only a part of a path invalidated by obstacles, and only this part needs replanning. Thus, in this paper a practical strategy was presented for motion planning in dynamic environments. First, a variant dynamic Rapidly-exploring Random Tree (RRT) planner was designed to combine global planning with local replanning. Then, the proposed dynamic RRT algorithm was implemented under Robotic Operation System (ROS) framework and applied to a 7-degree-of-freedom manipulator. Experimental results have shown that the proposed dynamic RRT planner can plan and execute movement of robot manipulator between a starting and target position while preserving task constraints and reacting to environment changes in real time.

Composite particle optimization with hyper-reflection scheme in dynamic environments

It is very difficult to design the learning structure of a robot beforehand in an unknown and dynamic environment, because the dynamics of the environment is unknown. Therefore, this paper proposes a spiking neural network (SNN) for behavior learning of a mobile robot in a dynamic environment including multiple moving obstacles. Furthermore, a behavior should be acquired and improved according to previously acquired behaviors. This indicates the robot should accumulate behavior knowledge through the lifetime learning. Therefore, we have proposed the concept of structured intelligence including three components; intuitive inference, logical inference, and self-consciousness. Intuitive inference is realized by neural computing based on numerical processing, while the logical inference is realized by fuzzy computing based on linguistic and symbolic processing. In this paper, we propose a learning method of SNN by using outputs from fuzzy controllers as teaching signals. The simulation shows the robot can extract and modifies the behavior knowledge of fuzzy controllers according to the spatiotemporal context of the facing environment.

New progress of lake sediments and environmental changes research in China

Anomaly detection involves identifying observations that deviate from the normal behavior of a system. One of the ways to achieve this is by identifying the phenomena that characterize “normal” observations. Subsequently, based on the characteristics of data learned from the “normal” observations, new observations are classified as being either “normal” or not. Most state-of-the-art approaches, especially those which belong to the family parameterized statistical schemes, work under the assumption that the underlying distributions of the observations are stationary. That is, they assume that the distributions that are learned during the training (or learning) phase, though unknown, are not time-varying. They further assume that the same distributions are relevant even as new observations are encountered. Although such a “stationarity” assumption is relevant for many applications, there are some anomaly detection problems where stationarity cannot be assumed. For example, in network monitoring, the patterns which are learned to represent normal behavior may change over time due to several factors such as network infrastructure expansion, new services, growth of user population, etc. Similarly, in meteorology, identifying anomalous temperature patterns involves taking into account seasonal changes of normal observations. Detecting anomalies or outliers under these circumstances introduces several challenges. Indeed, the ability to adapt to changes in non-stationary environments is necessary so that anomalous observations can be identified even with changes in what would otherwise be classified as “normal” behavior. In this paper, we proposed to apply weak estimation theory for anomaly detection in dynamic environments. In particular, we apply this theory to detect anomaly activities in system calls. Our experimental results demonstrate that our proposal is both feasible and effective for the detection of such anomalous activities.

Anomaly detection involves identifying observations that deviate from the normal behavior of a system. One of the ways to achieve this is by identifying the phenomena that characterize "normal" observations. Subsequently, based on the characteristics of data learned from the "normal" observations, new observations are classified as being either "normal" or not. Most state-of-the-art approaches, especially those which belong to the family parameterized statistical schemes, work under the assumption that the underlying distributions of the observations are stationary. That is, they assume that the distributions that are learned during the training (or learning) phase, though unknown, are not time-varying. They further assume that the same distributions are relevant even as new observations are encountered. Although such a "stationarity" assumption is relevant for many applications, there are some anomaly detection problems where stationarity cannot be assumed. For example, in network monitoring, the patterns which are learned to represent normal behavior may change over time due to several factors such as network infrastructure expansion, new services, growth of user population, etc. Similarly, in meteorology, identifying anomalous temperature patterns involves taking into account seasonal changes of normal observations. Detecting anomalies or outliers under these circumstances introduces several challenges. Indeed, the ability to adapt to changes in non-stationary environments is necessary so that anomalous observations can be identified even with changes in what would otherwise be classified as "normal" behavior. In this paper, we proposed to apply a family of weak estimators for anomaly detection in dynamic environments. In particular, we apply this theory to spam email detection. Our experimental results demonstrate that our proposal is both feasible and effective for the detection of such anomalous emails.

A dynamic environment, such as a production process, a communication network, highway traffic, etc., may contain a huge amount of information, changing with time, which is a valuable resource for understanding the general behavior of the environment, discovering the regularities and anomalies currently happening in the environment, controlling an evolution process, and intelligent modeling or managing the environment. Unfortunately, the data generated in a dynamic environment are often expressed in low level primitives and in huge volumes. Because of the dynamic, continuous and rapid changes of the information flow, it is difficult to catch the regularities and anomalies in a dynamic environment and react promptly for real-time applications. In this study, a knowledge discovery technique is integrated with data sampling and active database techniques to discover interesting behaviors of a dynamic environment and react intelligently to the environment changes. The discovery of the dynamics in a computer communication network and the application of the discovered knowledge for network management are taken as an example in our study. The study shows (1) data sampling is necessary in the collection of information for regularity analysis and anomaly detection; (2) knowledge discovery is important for generalizing low level data to high-level information and detecting interesting patterns; (3) active database technology is essential for real-time reaction to the changes in a dynamic environment; and (4) an integration of the three technologies forms a powerful tool for control and management of large dynamic environments in many applications.KeywordsKnowledge DiscoveryDynamic EnvironmentActive RuleEvolution RuleConditional EvaluatorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.