Molecular Computing

A number of nanomachines that cooperatively communicate and share molecular information in order to achieve specific tasks is envisioned as a nanonetwork. Due to the size and capabilities of nanomachines, the traditional communication paradigms cannot be used for nanonetworks in which network nodes may be composed of just several atoms or molecules and scale on the orders of few nanometers. Instead, molecular communication is a promising solution approach for the nanoscale communication paradigm. However, molecular communication must be thoroughly investigated to realize nanoscale communication and nanonetworks for many envisioned applications such as nanoscale body area networks, and nanoscale molecular computers. In this paper, a simulation framework (NanoNS) for molecular nanonetworks is presented. The objective of the framework is to provide a simulation tool in order to create a better understanding of nanonetworks and facilitate the development of new communication techniques and the validation of theoretical results. The NanoNS framework is built on top of core components of a widely used network simulator (ns-2). It incorporates the simulation modules for various nanoscale communication paradigms based on a diffusive molecular communication channel. The details of NanoNS are discussed and some functional scenarios are defined to validate NanoNS. In addition to this, the numerical analyses of these functional scenarios and their experimental results are presented. The validation of NanoNS is shown via comparative evaluation of these experimental and numerical results.

The paper recalls basic biological facts about living structures, as useful supports for molecular computing. Particular emphasis is made on membranes and proteins, whose structural and conformational dynamics might be considered as generating a computational environment in living systems.

This paper extends the framework of Machine Decision-Making Ability (MDMA) by relating it to Einstein's mass-energy equivalence equation, E = mc 2. MDMA quantifies a system's rate of autonomous state transitions, while E = mc 2 defines the total energy reservoir inherent in matter. We argue that energy is the ultimate substrate for decision-making: each autonomous transition requires a quantized energy expenditure, and the mass of a system therefore represents its absolute decision capacity. This paper establishes a conceptual and mathematical bridge: (i) MDMA measures the realized decision flow, (ii) E = mc 2 sets the theoretical ceiling, and (iii) physical limits such as Landauer's bound and Bremermann's rate provide the scaling laws between them. This alignment provides a unified framework for situating machine intelligence within physics itself.

The sequence design is a crucial problem in DNA based computation. We present results about tests carried on sets of DNA strands proposed in DNA computing papers since 1994. Our goal is to direct attention on the necessity of a middle-process between logical designing of input and practical computation in laboratory. The analysis shows that many input sets should not be used for a real DNA computation because they lead to a high probability of incurring in biological faults which result to be very unsafe for a reliable computation.

We review our recent efforts in building atom-scale quantumdot cellular automata circuits on a silicon surface. Our building block consists of silicon dangling bond on a H-Si(001) surface, which has been shown to act as a quantum dot. First the fabrication, experimental imaging, and charging character of the dangling bond are discussed. We then show how precise assemblies of such dots can be created to form artificial molecules. Such complex structures can be used as systems with custom optical properties, circuit elements for quantum-dot cellular automata, and quantum computing. Considerations on macro-to-atom connections are discussed.

This preprint explores how the Physics of Decisions shapes the limits and expression of intelligence. Two new extensions are introduced: (1) Einstein-MDMA Relativity, which links Machine Decision-Making Ability (MDMA) to relativistic and gravitational time dilation; and (2) Observable-DMA Collapse, which generalizes decision-making to all observers-living, artificial, or physical-and models observation itself as a form of state resolution. Together, these extensions propose that decisionmaking is not only a property of computing devices, but a physical phenomenon shaped by motion, gravity, and measurement.

This work extends the core Machine Decision-Making Ability (MDMA) framework into a broader scientific context by proposing a structured set of cross-disciplinary theoretical extensions. While the core preprint formalized MDMA as a measure of autonomous decision capacity grounded in physical state transitions, here we examine how fundamental constraints and affordances from physics, information theory, and complexity shape that capacity. We organize nine extensions into four families (Scaling Laws; Quantum and Relativistic Limits; Mind and Complexity; Post-Silicon Frontiers) and formulate each as an MDMA-aware principle with an associated research question. The goal is not to settle debates, but to offer a coherent roadmap for measurement, limits, and design that can guide future experimental and theoretical work across substrates.

This technical note addresses and refutes a series of recent misappropriations and distortions of the SFIT-XSM framework found in non-peer-reviewed speculative publications, particularly a paper titled "The Theory of Creation: Geometric Unification of Field Dynamics and Quantum Spin Topology" (Stallworth et al., 2025), published via Academia.edu. This document outlines the foundational principles of SFIT-XSM, its mathematically rigorous derivations, and testable predictions, and contrasts them against the nonrigorous, derivative reinterpretations that appear to rebrand core SFIT insights without acknowledgment or conceptual integrity. We reassert the authorship of key concepts involving scalar-phase fields, Möbius and braid-based spin structures, entropy-driven decoherence collapse, and quantum topological emergence, and offer both a historical timeline and formal definitions to distinguish legitimate SFIT development from recent distortions.

Molecular communication is an emerging communication paradigm for biological nanomachines. It allows biological nanomachines to communicate through exchanging molecules in an aqueous environment and to perform collaborative tasks through integrating functionalities of individual biological nanomachines. This paper develops the layered architecture of molecular communication and describes research issues that molecular communication faces at each layer of the architecture. Specifically, this paper applies a layered architecture approach, traditionally used in communication networks, to molecular communication, decomposes complex molecular communication functionality into a set of manageable layers, identifies basic functionalities of each layer, and develops a descriptive model consisting of key components of the layer for each layer. This paper also discusses open research issues that need to be addressed at each layer. In addition, this paper provides an example design of targeted drug delivery, a nanomedical application, to illustrate how the layered architecture helps design an application of molecular communication. The primary contribution of this paper is to provide an in-depth architectural view of molecular communication. Establishing a layered architecture of molecular communication helps organize various research issues and design concerns into layers that are relatively independent of each other, and thus accelerates research in each layer and facilitates the design and development of applications of molecular communication.

Introduction: Reframing Inertia through Scalar Resonance
In classical mechanics, inertia is treated as a property intrinsic to mass — an object's
resistance to acceleration. Within the Codex framework, we reframe this entirely: inertia is
not a fundamental attribute, but a relational effect resulting from resistance within
scalar resonance shells

we derive gravitational phenomena not from spacetime curvature, but as emergent effects of harmonic resonance shell structures in the scalar field Φ(x, t). Building on prior Codex Resonance principles, we show that gravitational attraction is a secondary result of scalar field tension gradients and nodal density interference. These effects form quantized resonance envelopes ("graviton shells") around mass-energy densities. We explain gravitational lensing, free-fall, and inertial mass without requiring Riemann curvature, recovering Einstein's predictions as limiting cases of scalar shell geometry.

We present a unified model based on the scalar field Φ(x, t), reconciling quantum spin, field dynamics, and particle behavior through geometric resonance structures. Instead of viewing particles as point-like, we describe them as stable patterns in a scalar field. This topological model explains mass, charge, and spin via field geometry and symmetry, offering an alternative to gauge-based frameworks.

Ab initio pseudopotentials for the IIA and IIB elements have been generated in the framework of density-functional theory, including gradient corrections to the local-density approximation (LDA). Their accuracy and transferability have been tested by extensive atomic computations. We applied these pseudopotentials to the evaluation of bonding properties of homonuclear dimers. Our molec- ular computations, not restricted to light elements, allow a wide assessment of the importance of gradient corrections in finite systems. For all the dimers considered here, the LDA error for bond energies is reduced by roughly 50%%uo. The relative improvement on equilibrium distances and vibra- tional frequencies is less impressive, but still important and systematic. We discuss the computational cost for the evaluation of gradient corrections in computer programs based on the plane-wave (or plane-wave-like) basis-set expansion.

3) kinase that acts as a logic "switch" for the engine of the molecular computer, which cooperates with the photosphases to realize the reversible computing. Here the true values of $\{\mathrm{T}, \mathrm{F}\}$ can be regarded to equal to the values of {1,0} under the context of specific application problems. In order to describe the computation in a biochemical way, the main operations can be summarized as: The $\mathrm{b}\mathrm{i}\mathrm{o}$ -operations(ors) includes: * The phosphorlation-dephosphorylation for encoding the objects as the binary logic values. * The activation of the combinatorial forms of the objects coded by phosphorlation- dephosphorylation. * The regulation of the directed flow of signaling molecules at the inter-and-intra cell communication mode and among the channels. * The biochemical reaction of the signaling molecules within the domain of the corresponding pathways with kinases. * The activation of the kinases and corresponding pathways $*$ The readout. These are given in the meaning of the direct (potential) implementation of the molecular computers, where we are developing the self-regulation schemes for efficiently controlling the information flow with engineered phosphorylati0-dephosphorylation mechanism. The high-level operations(ors) includes: * DISTRIBUTE: to put the population -the set of the candidates -into the concerned cells. * FEED: to push these candidates into the pathways. * SEVE: to select the valid candidates through the pathways -the constraints of the computation.

An intramolecular computing model is presented that is based on the quantum time evolution of a single molecule driven by the preparation of a non-stationary single-electron state. In our scheme, the input bits are encoded into the coupling constants of the Hamiltonian that governs the molecular quantum dynamics. The results of the computation are obtained by carrying out a quantum measurement on the molecule. We design reliable and, xor, and half-adder logic gates. This opens new avenues for the design of more complex logic circuits at a single-molecular scale.

This study was performed to determine the molluscs distributed in Iskenderun Bay (Levantine Sea). For this purpose, the material collected from the area between the years 2005 and 2009, within the framework of different projects, was investigated. The investigation of the material taken from various biotopes ranging at depths between 0 and 100 m resulted in identification of 286 mollusc species and 27542 specimens belonging to them. Among the encountered species, Vitreolina cf. perminima (Jeffreys, 1883) is new record for the Turkish molluscan fauna and 18 species are being new records for the Turkish Levantine coast. A checklist of Iskenderun mollusc fauna is given based on the present study and the studies carried out beforehand, and a total of 424 moluscan species are known to be distributed in Iskenderun Bay.

In this note we consider a new variant of network of splicing processors which simplifies the general model such that filters remain associated with nodes but the input and output filters of every node coincide. This variant is called {\it network of uniform splicing processors}. Although the communication in the new variant seems less powerful, being based on simpler filters, the new variant is sufficiently powerful to be computationally complete. The main result is that nondeterministic Turing machines can be simulated by networks of uniform splicing processors. Furthermore, the simulation is time efficient.

In this paper, we consider the computational power of a new variant of networks of splicing processors in which each processor as well as the data navigating throughout the network are now considered to be polarized. While the polarization of every processor is predefined (negative, neutral, positive), the polarization of data is dynamically computed by means of a valuation mapping. Consequently, the protocol of communication is naturally defined by means of this polarization. We show that networks of polarized splicing processors (NPSP) of size 2 are computationally complete, which immediately settles the question of designing computationally complete NPSPs of minimal size. With two more nodes we can simulate every nondeterministic Turing machine without increasing the time complexity. Particularly, we prove that NPSP of size 4 can accept all languages in NP in polynomial time. Furthermore, another computational model that is universal, namely the 2-tag system, can be simulated by NPSP of size 3 preserving the time complexity. All these results can be obtained with NPSPs with valuations in the set fÀ1; 0; 1g as well. We finally show that Turing machines can simulate a variant of NPSPs and discuss the time complexity of this simulation.

We present a variant of the Q-learning algorithm with automatic control of the exploration rate by a competition scheme. The theoretical approach is accompanied by systematic simulations of a chaos control task. Finally, we give interpretations of the algorithm in the context of computational ecology and neural networks.

The use of synthetic DNA molecules for computing provides various insights to evolutionary computation. A molecular computing algorithm to evolve DNA-encoded genetic patterns has been previously reported in [1], [2]. Here we improve on the previous work by studying the convergence behavior of the molecular evolutionary algorithm in the context of text classification problems. In particular, we study the error reduction behavior of the evolutionary learning algorithm, both theoretically and experimentally. The individuals represent decision lists of variable length and the whole population takes part in making probabilistic decisions. The evolutionary process is to change each individual towards correct classification of training data, which is based on an error minimization strategy. The evolved molecular classifiers show a performance competitive to the standard algorithms such as naïve Bayes and neural network classifiers on the data set we studied. The possibility of molecular implementation by use of DNA-encoded individuals combined with simple molecular operations on a very big population distinguishes this approach from other existing evolutionary algorithms.

We use molecular computation to solve pattern classification problems. DNA molecules encode data items and the DNA library represents the empirical probability distribution of data. Molecular bio-lab operations are used to compute conditional probabilities that decide the class label. This probabilistic computational model distinguishes itself from the conventional DNA computing models in that the entire molecular population constitutes the solution to the problem as an ensemble. One important issue in this approach is how to automatically learn the probability distribution of the DNA-based classifier from observed data. Here we develop a molecular evolutionary algorithm inspired by directed evolution, and derive its molecular learning rule from Bayesian decision theory. We investigate through simulation the convergence behaviors of the molecular Bayesian evolutionary algorithm on a concrete problem from statistical pattern classification.

We consider a uniform way of treating objects and rules in P systems: we start with multisets of rules, which are consumed when they are applied, but the application of a rule may also produce rules, to be applied at subsequent steps. We find that this natural and simple feature is surprisingly powerful: systems with only one membrane can characterize the recursively enumerable languages, both in the case of rewriting and of splicing rules; the same result is obtained in the case of symbol-objects, for the recursively enumerable sets of vectors of natural numbers.

Abstract-In recent works for high-performance computing, computation with DNA molecules, ie DNA computing, has considerable attention as one of non-silicon-based computing. Watson–Crick complementarity and massive parallelism are two important features of ...

We conducted calculations on the quantum transport properties of an Au/InP-nanowire/Au junction device, considering explicit charge doping of the InP-nanowire channel in non-equilibrium Green function scheme [1]. This analysis aims to elucidate the impact of excess charge carriers on the channel's quantum transport properties within the device. Planar devices constructed from elements in the III-V group currently operate very close to the ballistic limit, unlike Si devices. Therefore, it is important to envisage the transmission properties of III-V nanowire devices equipped with metal contacts under bias and gate voltages with excess charge doping. We discovered that excessive charge doping in the InP-nanowire channel can degrade current flow, leading to the onset of Coulomb blockade at the interface between the InP-nanowire and Au contacts. Additionally, Coulomb blockade manifests as charge localization, resulting in significantly reduced mobilities, reaching as low as 530 cm2V-1s-1. As a result of charge localization and excessive charging of the channel, the transmission probability cannot reach 1 even under higher biases (1 V), with only tunneling currents present.

In this note we consider a new variant of network of splicing processors which simplifies the general model such that filters remain associated with nodes but the input and output filters of every node coincide. This variant is called {\it network of uniform splicing processors}. Although the communication in the new variant seems less powerful, being based on simpler filters, the new variant is sufficiently powerful to be computationally complete. The main result is that nondeterministic Turing machines can be simulated by networks of uniform splicing processors. Furthermore, the simulation is time efficient.

Alcohol abuse is a major problem in the United States. The most common measurement tool used in law enforcement for the determination of alcohol concentration in subjects is the breath analyzer (BrAC), and subjects know when they are being measured. However , when surrept i t ious measurements of BrAC in freely moving humans are sought, then hyperspectral remote-sensing measurement techniques must be used. In this work, two remote sensing methods for the estimation of BrAC were studied. A clinical trial involving human subjects was performed to assess the supposition that molecular factor computing (MFC) near-infrared (NIR) hyperspectral imaging and speech analysis using laser interferometry could be used to noninvasively estimate BrAC in subjects at a distance. A conventional breath analyzer was used to prov ide reference measurements. MFC NIRS imaging measurements gave a standard error of prediction (SEP) for a global model (all subjects) for blood alcohol of 8.1 mg/dL (0.0081%) a...

The configurations of single and double bonds in polycyclic hydrocarbons are abstracted as Kekulé states of graphs. Sending a socalled soliton over an open channel between ports (external nodes) of the graph changes the Kekulé state and therewith the set of open channels in the graph. This switching behaviour is proposed as a basis for molecular computation. The proposal is highly speculative but may have tremendous impact. Kekulé states with the same boundary behaviour (port assignment) can be regarded as equivalent. This gives rise to the abstraction of Kekulé cells. The basic theory of Kekulé states and Kekulé cells is developed here, up to the classification of Kekulé cells with ≤ 4 ports. To put the theory in context, we generalize Kekulé states to semi-Kekulé states, which form the solutions of a linear system of equations over the field of the bits 0 and 1. We briefly study so-called omniconjugated graphs, in which every port assignment of the right signature has a Kekulé state. Omniconjugated graphs may be useful as connectors between computational elements. We finally investigate some examples with potentially useful switching behaviour.

An analysis is given of the paradox of an inverted temperature profile in the vapor phase between an evaporating and a condensing liquid surface. This analysis is based on a description in the context of nonequilibrium thermodynamics for a two-phase system. Conditions for the occurrence of both the inverted temperature profile and for supersaturation near the evaporating surface are then given in terms of the temperature and pressure jump coefficients appearing in this description. Comparing with the description using kinetic theory in the vapor phase we obtain explicit expressions for these jump coefficients. Using these expressions the conditions found using nonequilibrium thermodynamics reduce to those given in the context of kinetic theory.

In this paper, a novel readout approach for the Hamiltonian Path Problem (HPP) in DNA computing based on the real-time polymerase chain reaction (PCR) is proposed. Based on this approach, real-time amplification was performed with the TaqMan probes and the TaqMan detection mechanism was exploited for the design and development of the proposed readout approach. The readout approach consists of two phases: real-time amplification in vitro using TaqManbased real-time PCR, followed by information processing in silico to assess the results of real-time amplification, which in turn, enables the extraction of the Hamiltonian path. In this paper, instead of using manual clustering of two different reactions in real-time PCR, a computer system is developed such that the clustering can be done automatically. This clustering process can be done quickly based on the number of nodes in the HPP problems and the TaqMan reactions and the data obtained previously is used to extract the correct Hamiltonian path.

DNA strand displacement circuits are powerful tools that can be rationally engineered to implement molecular computing tasks because they are programmable, cheap, robust and predictable. A key feature of these circuits is the use of catalytic gates to amplify signal. Catalytic gates tend to leak, that is, they generate output signal even in the absence of intended input. Leaks are harmful to the performance and correct operation of DNA strand displacement circuits. Here, we present "shadow 1

In this paper, we investigate the possible scenarios in which a number does not satisfy the Collatz Conjecture, or the 3x+1 conjecture. Specifically, we examine the algebraic behavior of possible counterexamples of the Collatz Conjecture which may lead to a loop other than the trivial loop or unbounded divergence. In order to investigate these, we examine the parity of the numbers in a general Collatz reduction sequence. Further, we examine cases in which these parity cycles are infinitely periodic with finite period. Through the research conducted in the paper, we formulate a necessary algebraic condition for looping in the Collatz Conjecture. We also prove that if a number has a diverging reduction sequence, then it must generate an infinite aperiodic parity cycle.

We have previously proposed a way of using coupled quantum dots to construct digital computing elements-quantum-dot cellular automata (QCA). Here we consider a different approach to using coupled quantum-dot cells in an architecture which, rather that reproducing Boolean logic, uses a physical near-neighbor connectivity to construct an analog Cellular Neural Network (CNN).

The structural and electronic properties of melatonin and its six hydroxy isomers have been investigated theoretically by performing semi-empirical and ab initio molecular orbital theory calculations. The geometry of the systems has been optimized considering the semi-empirical molecular orbital theory at the level of AM1, and the electronic properties of the systems have been calculated by ab initio RHF including full MP2 correlation correction in their ground state. Conclusions were drawn by comparing with experimental results.

The structural and electronic properties of per¯uorinated surfactants per¯uorooctane sulfonate (PFOS) and lithium per¯uorooctane sulfonate (LiPFOS) have been investigated theoretically by performing semi-empirical molecular orbital theory at the level of AM1 calculations. The optimized structure and the electronic properties of the molecules are obtained.

Over the past few decades numerous attempts have been made to solve combinatorial optimization problems that are NP-complete or NP-hard. It has been evidenced that DNA computing can solve those problems which are currently intractable on even fastest electronic computers. This paper proposes a new DNA algorithm for solving N-Queen problem, a complex optimization problem. The algorithm not only shows whether or not a solution exists, but provides all possible solutions by massively parallel computations. The proposed algorithm can be easily extended to solve other optimization problems.

The conformational analysis of N-formyl-D-serine-D-alanine-NH2 dipeptide was comprehensively studied using the density functional theory methods in the gas and solution phases. The all-expected 3 5 =243 stable conformers were explored, where 91 conformers were located, and the rest of them were migrated to the more stable geometries. Migration pattern suggests the more stable dipeptide model with the serine in βL, γD, γL and the alanine in γL and γD configurations. The investigation of side-chain-backbone interactions revealed that the most stable conformer, γD-γL, is in the β−turn region of the Ramachandran map; therefore, serinealanine dipeptide model should be adopted with a β−turn conformation. QTAM consideration of the intramolecular hydrogen bonding in β-turns disclosed the highest stable conformer as γD-γL includes the three hydrogen bonds. The computed UV-Vis spectrum alongside of NBO calculation showed and explained the five main electronic transition bands derived of n→ n * of intra-ligand alanine moiety of dipeptide structure.

The conformational analysis of N-formyl-D-serine-D-alanine-NH2 dipeptide was comprehensively studied using the density functional theory methods in the gas and solution phases. The all-expected 3 5 =243 stable conformers were explored, where 91 conformers were located, and the rest of them were migrated to the more stable geometries. Migration pattern suggests the more stable dipeptide model with the serine in βL, γD, γL and the alanine in γL and γD configurations. The investigation of side-chain-backbone interactions revealed that the most stable conformer, γD-γL, is in the β−turn region of the Ramachandran map; therefore, serinealanine dipeptide model should be adopted with a β−turn conformation. QTAM consideration of the intramolecular hydrogen bonding in β-turns disclosed the highest stable conformer as γD-γL includes the three hydrogen bonds. The computed UV-Vis spectrum alongside of NBO calculation showed and explained the five main electronic transition bands derived of n→ n * of intra-ligand alanine moiety of dipeptide structure.

The University of Lampung area, especially in the Faculty of ISIP, FEB, and FT is a densely packed student area. This area has undergone many land-use changes. The condition of the land as a green open space has changed its function to become an area for lecture buildings and offices. One of the impacts is an increase in direct surface runoff and a decrease in the quantity of water that seeps into the ground, this condition causes flooding during the rainy season. To facilitate the rehabilitation of the drainage system in the University of Lampung area, it is necessary to redesign the drainage system of the University of Lampung area. Rehabilitation of drainage channels is carried out to resolve flood inundation points that occur during the rainy season. Rainwater that is channeled through drainage channels is directed to natural or artificial reservoirs. The collected rainwater is used to recharge groundwater through natural infiltration methods. The analysis carried out in this st...

A massive of early drug tests implies that there exist strong inner relationships between the bio-medical and pharmacology characteristics of drugs and their molecular structures. The forgotten topological index was defined to be used in the analysis of drug molecular structures, which is quite helpful for pharmaceutical and medical scientists to grasp the biological and chemical characteristics of new drugs. Such tricks are popularly employed in developing countries where enough money is lacked to afford the relevant chemical reagents and equipment. In our article, by means of drug molecular structure analysis and edge dividing technology, we present the forgotten topological index of several widely used chemical structures which often appear in drug molecular graphs.

DNA computing is a new kind of computation for solving the complex problems with the huge degree of parallelism. Recently it is found that DNA-based logic systems can be useful in many of biomedical applications such as early cancer detection. DNA logic systems have been applied successfully to detect the risky patterns of nucleotide-based cancer biomarkers (microRNAs). Detection of real diseases requires the large-scale DNA-based logical systems. Therefore, large-scale DNA-based logic circuits is a crucial research topic. In this paper, an automatic design flow is proposed to facilitate the design, verification and physical implementation of multi-stage and large-scale DNA logic circuits. Digital Microfluidic Biochips (DMFB) have been used recently as a promising platform for efficient implementation of DNA-based computing systems and circuits. We used this technology as the physical platform for implementation of DNA-based circuits. Our experiments and implementations show the feasibility, accuracy, efficiency and simplicity of the proposed design flow. Final DNA reactions that are synthesized by the proposed design flow are verified and simulated with stochastic DNA-reaction simulators to prove the correctness of the proposed design flow. This design flow can open a new horizon for researchers and scientists to design, implement and evaluate the DNA-based logic systems.

This paper studies Genetic Programming (GP) and its relation to the Genetic Algorithm (GA). GP uses a GA approach to breed successive populations of programs, represented in the chromosomes as parse trees, until a program that solves the problem emerges. However, parse trees are not naturally homologous, consequently changes had to be introduced into GP. To better understand these changes it would be instructive if a canonical GA could also be used to perform program induction. To this end an appropriate GA representation scheme is developed (called EP-I for Evolutionary Programming with Introns). EP-I has been tested on three problems and performed identically to GP, thus demonstrating that the changes introduced by GP do not have any properties beyond those of a canonical GA for program induction. EP-I is also able to simulate GP exactly thus gaining further insights into the nature of GP as a GA.

Nanoelectronics presents the opportunity of incorporating billions of devices into a single system. Its opportunity is also its challenge: the economic design, verification, manufacturing, and testing of billion component systems. In this presentation I will explore how the abstractions used in computer systems change as we approach nanoscale dimensions.