Self-Evolutionary Component Algorithm

• Self-evolutionary component algorithms are adaptive computational frameworks that evolve key components—such as population topology, control parameters, and representation—as the algorithm runs.
• They dynamically reorganize structures and self-adjust mutation and recombination rates to enhance search efficiency, diversity, and robustness compared to static evolutionary algorithms.
• These methods have been applied in areas ranging from self-organizing topologies and parameter control to autonomous software systems, showcasing resilience and improved performance in complex, dynamic tasks.

A self-evolutionary component algorithm is a class of computational method or framework wherein the algorithm is architected so that its key structures or parameters—including population topology, control parameters, representation schemes, or functional modules—adapt, reorganize, or evolve as the algorithm runs, typically via mechanisms that mimic natural evolution, self-organization, or dynamic adaptation. This approach stands in contrast to traditional evolutionary algorithms (EAs) with fixed populations, static parameter schedules, or predetermined operators. Self-evolutionary component algorithms have been proposed and explored in a range of contexts, including population topology adaptation, self-adaptive parameter control, modularity and concurrency in artificial life, evolutionary control in multi-objective or dynamic environments, and the explicit incorporation of non-genetic inheritance mechanisms. The core principle is that components of the computational system are both the subject and the object of evolution or adaptation, resulting in systems characterized by greater robustness, diversity, flexibility, and, in many cases, improved search efficiency or problem-solving performance.

1. Topologically Self-Organizing Evolutionary Algorithms

One principal instantiation is the Self-Organizing Topology Evolutionary Algorithm (SOTEA), which eschews fixed panmictic or cellular topologies by letting the interaction network between candidate solutions (individuals) evolve through simple, local, and fitness-sensitive structural rules (0907.0516). In SOTEA, each individual is represented as a node in a dynamic graph; network edges are added or removed through mechanisms tightly coupled to evolutionary state and competition.

Reproduction is implemented such that each offspring node connects to its parent and may inherit a subset of its parent's links (with probability $P_{add}$), or cause the parent to drop some links ($P_{remove}$). During selection, a random node competes with its least-fit neighbor (using contextual fitness measured relative to local graph neighbors); the loser is removed, with its links transferred to the winner. Connectivity is further adaptively managed via a quadratic set-point function $K_{set}$ based on global fitness rank, and modularity is encouraged by manipulating the local clustering coefficient, with links between high-fitness nodes weighed less.

The population topology thus self-organizes to display features typical of biological systems: logarithmic characteristic path length with respect to population size, heavy-tailed degree distributions, and anti-correlated clustering-degree relationships (low-clustering high-degree hubs and tightly-clustered low-degree subgraphs). These topological structures directly impact genetic search: SOTEA preserves diversity, prevents dominance by a few individuals, filters out neutral (uninformative) events via the Event Takeover Value (ETV) measure, and accentuates rare but consequential evolutionary events. Empirical results indicate that SOTEA outperforms classic panmictic and cellular EAs on both smooth and rugged fitness landscapes, with enhanced diversity and a more robust, distributed search.

2. Self-Adaptive Parameter Control and Representation

In self-evolutionary component algorithms, evolutionary operations or control parameters—including mutation rates, recombination strategies, and even encoded representations—are subject to adaptation. This approach is typified by self-adaptive EAs in which mutation strengths are encoded in the genome and evolve concomitantly with solution variables (Case et al., 2020, Doerr et al., 2017, Rajabi et al., 2020, Ye et al., 2023, Rajabi et al., 2020).

For example, in hybrid self-adaptive evolutionary algorithms (HSA-EA) for graph 3-coloring, candidate solutions encapsulate both problem variables (e.g., real-coded vertex weightings) and mutation parameters, allowing both to evolve via stochastic and locally adaptive update rules (Fister et al., 2013). The mutation parameter $q_i$ is updated each generation as $$q_{i}^{(t+1)} = q_{i}^{(t)} \exp(\tau' N(0,1) + \tau N_i(0,1))$$, with $y_i^{(t+1)} = y_i^{(t)} + q_{i}^{(t+1)} N_i(0,1)$, where $N(0,1)$ denotes standard normal noise, and bounds on $q_i$ prevent premature stagnation (Eq. 3–5).

This self-adaptation is not confined to single-objective contexts: multi-objective EAs now integrate per-individual or population-level self-adaption of mutation strengths, using schemes such as two-rate adaptation, log-normal sampling, or variance-controlled update rules, guided by indicators like hypervolume, inverted generational distance, or objective improvement (Ye et al., 2023). These schemes have been shown to significantly accelerate convergence and enhance the diversity of Pareto front solutions in classic benchmark problems.

Self-adaptive mutation mechanisms also extend to multidimensional settings, such as evolutionary clustering of high-dimensional data (Xu et al., 2019), and to hyper-parameter optimization in deep model-based Pareto front learning (Chang et al., 2021), where evolutionary strategies are employed to perform gradient-based adaptation of conditioning parameters.

3. Modularity, Concurrency, and Artificial Organism Design

Another dimension of self-evolutionary component algorithms is the architectural self-organization and modular, concurrent execution exemplified by object-oriented combinator chemistry-based artificial organisms (Williams, 2018). Here, executable modules ("methods") are composed from primitive combinators, supporting duplication, specialization, and open-ended complexity increase. Concurrency is intrinsic: methods are asynchronously and partially ordered, yielding redundancy, degeneracy, and parallelism, with the aim of amortizing the cost of increased complexity. Spatial organization, realized as a “roving pile” of interacting combinator groups, supports distributed self-replication, autocatalysis, and asynchronous message passing, reminiscent of natural plasmid distribution and regulatory networks.

Such architectures highlight the importance of component-level modularity: the ability to "plug in" new behaviors or functionalities without redesigning the entire system, as well as to compartmentalize replication and computation. The mathematical modeling of replication time in terms of module number, gene length, and parallel resource allocation—$O(MN/B)$, where $B$ is the number of replication process instances—exposes how complexity growth is practically managed via concurrency.

4. Evolutionary Adaptation in Cellular Automata and Collective Systems

Self-evolutionary algorithms also appear in the design of distributed systems such as self-organizing cellular automata and swarm-based collectives. Evolutionary algorithms are used to discover local update rules (encoded as finite-state machines with internal memory) that confer both self-adaptive and self-organizing properties (Knoester et al., 2014). Each cell locally executes a genome-specified FSM, whose logic gates operate on both neighborhood input and hidden state variables. Such systems have demonstrated state-of-the-art performance in density classification tasks across 1D, 2D, and 3D CA, with evidence of scalability and resilience due to the use of hidden state (self-adaptation) and communication (self-organization), as quantified by empirical measures such as $S_{op}(x)$.

In morphogenetic collective systems, component heterogeneity, dynamic differentiation/re-differentiation, and local information sharing drive the emergence of complex and self-repairing patterns (Sayama, 2018). Behavioral update functions $F$ and type update functions $G$ are parameterized by both individual and neighbor state vectors, allowing swarming agents to adapt in response to environmental cues and neighbor states, and to maintain diversity and robustness in evolving spatial structures.

5. Non-Genetic Inheritance: Epigenetic and Extended Synthesis Mechanisms

Self-evolutionary component algorithms may transcend classical genetic paradigms by incorporating mechanisms from the Extended Evolutionary Synthesis, in particular, epigenetic inheritance (Yuen et al., 2021). In this context, solutions are augmented with epigenotypes—layers of inherited yet reversible "tags" or "marks" that regulate gene expression in response to environmental pressures. Unlike standard genetic operators, these mechanisms enable rapid, non-genotypic adaptation ("switching" genes on or off); this facilitates faster convergence, adaptability, and robustness to dynamic environments, while safeguarding genetic diversity. Most extant evolutionary algorithms have yet to realize full epigenetic operator design, as current approaches (e.g., probabilistic gene masking via cytosine methylation) do not fully recapitulate the dynamic, conditional, and inheritable nature of biological epigenetic systems.

6. Self-Evolution in Systems Software—Autonomous and Online Evolutionary Engines

The vision of self-evolving computing systems generalizes these concepts to adaptive software architectures capable of responding autonomously to unanticipated conditions or goals (Weyns et al., 2022). Such systems embed an evolutionary engine within their control loop, triggering online search over architectures when anomalies or new objectives are detected. Candidates are constructed from modular computing elements (supplied by "computing warehouses" with standardized specifications and integration guides), and evolved via evolutionary algorithms (e.g., $(1,\lambda)$-EA), with selection based on system-level performance metrics. These architectures demand novel representations of system self-models, sandboxed experimentation, and integration of humans-in-the-loop only for high-level constraint setting or feedback.

7. Summary Table: Self-Evolutionary Component Principles Across Domains

Principle	Example Mechanism	Impact on Algorithm
Topological self-organization	Dynamic network rewiring in SOTEA	Promotes diversity, robustness, modular search
Self-adaptive parameter control	Encoded, evolving mutation/crossover	Adaptive search efficiency, reduced manual tuning
Modular concurrent execution	Asynchronous methods on "roving pile"	Scalability, redundancy, evolvability
Genotype-epigenotype separation	Epigenetic tags regulating expression	Fast adaptation, stability under dynamic environments
Local-reaction/collective emergence	Self-organizing CAs, swarm behaviors	Distributed adaptation, resilience, emergent patterning
Online evolutionary engine	System architecture re-engineering	Real-time response to anomalies/goals, autonomous evolution

8. Impact, Limitations, and Future Directions

Self-evolutionary component algorithms have demonstrated empirical and theoretical advantages in preserving diversity, sustaining robust search, and adapting to changing or multi-objective landscapes without reliance on external parameter tuning. The modular, distributed, and adaptive qualities of such systems position them as candidates for constructing scalable, resilient artificial life and adaptive software. However, practical implementation challenges remain: designing algorithms that reliably balance exploration and exploitation, defining effective inheritance mechanisms (epigenetic or modular), and establishing principled interfaces for reusable and auto-evolvable computing components. The intersection of evolutionary principles from biology (extended synthesis) and modern computational frameworks continues to motivate research toward increasingly complex, adaptable, and autonomous self-evolutionary systems.