Evolutionary Logic

Primary technical source: van den Dorpel's interview with Julian F. Miller, inventor of Cartesian Genetic Programming (CGP). See also Spike #70 interview with Tina Rivers Ryan.

The algorithm: Cartesian Genetic Programming

The specific algorithm powering Mutant Garden (and informing Death Imitates Language) is Cartesian Genetic Programming, developed by Julian F. Miller and Peter Thomson at Napier University in the late 1990s for industrial circuit board optimisation. Van den Dorpel encountered it in 2019 and implemented his own version in TypeScript.

CGP represents a computational programme as a two-dimensional grid of nodes — small functions (add, subtract, multiply, etc.). A chromosome is a string of genes encoding which nodes connect to which. Key properties:

Safe mutation: the graph structure guarantees that no mutation can produce an infinite loop. This solved van den Dorpel's central technical problem: most mutations of raw computer code cause crashes (the halting problem). The graph encodes only what can safely terminate.
Non-coding genes: the decoding process ignores some genes entirely — they contribute nothing to the current computation. But they persist, mutate, and after several generations may activate and produce new structures. This is latent potential: a genetic archive of dormant possibilities that evolution can draw on.
Multiple outputs: a single chromosome can solve more than one problem simultaneously — each output is a different solution emerging from the same generative structure.

CGP belongs to a lineage: Friedberg (1958, random program mutation), Koza (1992, tree-based GP), and CGP (1997) as a graph-based alternative, developed alongside Push and Grammatical Evolution.

The halting problem as the condition of creative mutation

Van den Dorpel's original ambition was to mutate computer code rather than just data — to let the programme itself evolve, not just its outputs. The obstacle: "most mutations of computer code cause infinite loops, stack overflows, rainbow spinners, blue screens of death, and core dumps." This is the halting problem — provably undecidable.

CGP's graph topology is the engineered solution. By constraining mutation to a grid of safe computational operations, the system can mutate freely without risk of non-termination. The creative freedom (unlimited mutation) is enabled by a structural limit (the graph).

This echoes the stop-condition problem in recursion: unchecked self-reference destroys itself. The solution in both cases is an internal architectural limit — not an external prohibition but a structural feature that keeps the process alive by preventing it from consuming itself.

Fitness functions: the mechanics of selection

Without a fitness function, evolutionary systems produce variation without direction — "always different, always the same." The fitness function gives selection a criterion: it defines what "better" means for a given generation. Van den Dorpel has formalised different theories of "better" across different works:

Work	Fitness function
Death Imitates Language (2016)	Manual — the artist decides which specimens live and die
Hybrid Vigor (2017)	Crowdsourced — the public decides
Nested Exchange (2017)	The "hipster algorithm" — maximise difference from all others in the population
Mutant Garden (2019–)	Complexity of construction (render time) vs. complexity of experience (file size after compression)

The progression is a systematic externalisation of taste into code. Each version encodes a different theory of value: subjective (manual), collective (crowdsourced), structural-relational (difference), formal (complexity ratio). The fitness function is formalised taste — the criterion that process-legibility identifies as what thirty years of practice accumulates and what AI cannot replicate.

Vvgdamn Pipikaka Yozdczmi — Death Imitates Language — *Vvgdamn Pipikaka Yozdczmi* (2017) — *Death Imitates Language*; manual fitness: artist selects which organisms live and die

*Hybrid Vigor .bio* (2017) — crowdsourced fitness: public selection

R. Electrotype — Nested Exchange — *R. Electrotype* (2018) — *Nested Exchange*; the "hipster algorithm": maximise difference from all others

Triddle — Mutant Garden — *Triddle* (2020) — *Mutant Garden*; formal fitness: construction complexity vs. experience complexity

Non-coding genes: latent potential as archive

The non-coding gene is philosophically the most significant feature of CGP. Genes that currently contribute nothing to the computation persist in the chromosome, accumulate mutations, and can suddenly become active — producing new structures from what was dormant.

This is a biological archive of unused possibilities. The organism doesn't express them now, but they are not lost — they are carried forward, transformed, and available for future activation. Applied to an artwork: the generative system contains more possibilities than it currently expresses. The visible output is a surface on top of a much larger latent space.

This connects to mediation and the archive: the archive carries what is not currently active but shapes what can emerge. The non-coding gene is an archival structure within the algorithm itself — the past encoded into a form that can become the future.

Death as selection pressure

In Death Imitates Language, the artist decides which organisms live and die. The death of an organism is not failure but selection pressure — it clears space for fitter configurations and drives the system toward complexity. This is precisely the reframing in senescenence: death as system feature, not system failure.

The cellular automaton and the genetic algorithm share this logic. Both are systems in which death is generative: the cell that dies according to the automaton's rules, the organism that is culled by the fitness function, are both participating in the system's motion toward greater complexity.

The artist's role in both is the same: not to prevent death but to set the conditions under which death is productive. "Permitted" rather than "composed."

Life as 3.5 billion years of R&D

Julian Miller's closing formulation: "Life is a 3.5 billion year old technology."

Biological life is not the opposite of computation — it is its oldest instance. Evolution is R&D at geological timescale: mutation as random variation, natural selection as fitness function, reproduction as inheritance. What CGP compresses into rendering cycles is a recapitulation of the same logic that produced all living things.

This reframes the biological jargon question that van den Dorpel raises in the interview. The terms (mutation, crossover, breeding, fitness) are not metaphors imported from biology — they are the literal operations, abstracted. The question is not whether the algorithm is "alive" but whether life is, at some level, algorithmic. Miller's answer: studying evolutionary algorithms produces "a huge amount of respect and wonder at all living things." The bottom-up approach deepens, rather than demystifies, the question of what life is.

For the practice, this means: Mutant Garden is not a simulation of evolution. It is evolution, running at a different substrate and timescale, with a different fitness function. The organisms it breeds are not metaphors for life — they are an instance of the same process.

Algorithmic archaeology

CGP was developed for industrial circuit board optimisation in the 1990s. Van den Dorpel repurposed it for art in 2019. The algorithm carries its origin: it is shaped by the engineering problems it was built to solve, the computational constraints of the 1990s, the specific choices Miller and Thomson made in designing the graph representation.

To use CGP for art is to excavate it — to find an algorithm within its historical layer and redeploy it in a context its creators did not anticipate. The artwork is partly defined by the contingent history of its technical substrate. This is the algorithmic archaeology argument: code is not objective and universal but is a historical assemblage, shaped by the specific situation of its production.

Van den Dorpel on the scale of legacy code (Spike #70): "you can see remnants of Windows 3.1; it's still there." Code accumulates. New systems are built on old foundations that cannot be fully comprehended, let alone replaced. "Code isn't law, but rather destiny."

Extending to pre-computational art (Cloud Writings, 2026)

The Cloud Writings exhibition text (Takuro Someya Contemporary Art, Tokyo, 2026) expands the concept of algorithmic archaeology beyond software history into art history:

"Since 2019, I have dedicated myself to researching artists such as Anni Albers, Vera Molnár, and Charlotte Posenenske, who are known for their systematic approach in creating work with two-dimensional grids, even before the computer as we know it was invented. I develop contemporary algorithms inspired by these historical ones — a methodology I call 'algorithmic archeology'."

This is a second sense of algorithmic archaeology. The Spike #70 sense is excavation: finding an existing algorithm (CGP) within its historical layer and rediscovering what it can do in a different context. The Cloud Writings sense is generative: researching historical methodologies — systematic, rule-based, grid-based practices developed before or alongside the computer — and developing new algorithms inspired by them.

*Anobium* (2025) — Sakura fineliner on Hahnemühle watercolour paper, 70×90cm; the woodworm as figure for the algorithm working from within the grid

The three named precursors:

Anni Albers (1899–1994): Bauhaus weaver and textile theorist; loom as binary computer; structure as the material of thought. The Anni series and Beads works carry this lineage in name and form.
Vera Molnár (1924–2023): founding figure of computer art; rule-based systems before computers. Pression des pairs (2024) is explicitly derived from her 1959 Quatre éléments distribués au hasard.
Charlotte Posenenske (1930–1985): German Minimal/Conceptual artist; serialized, modular, reproducible forms; work that dissolves individual authorship into structural instruction.

All three worked systematically without computers, or alongside their emergence. All three are women. The acknowledgment is explicit: "an effort to identify and honor my influences." Algorithmic archaeology here is also an act of lineage — naming the pre-digital systematic tradition that the generative practice continues.

The two senses of algorithmic archaeology are complementary: one excavates within technical history (CGP, Windows 3.1 sediment); the other excavates within art history (Albers, Molnár, Posenenske as systematic predecessors). In both cases the past is not nostalgia but active material — what existed then generates what can be made now.

The Angles Morts exhibition catalogue (LOHAUS SOMINSKY, 2024) adds a fourth named precursor: Tauba Auerbach (b. 1981), who extends the female generative lineage into the contemporary moment — geometric abstraction, optics, weaving, systemic visual logic. And it makes the gender dimension explicit:

"It should be noted that the artists listed are all women. In his research, Van den Dorpel recognises a dominance of female artists in the field of generative art. He considers a historical connection between the systematic arrangement of recurring elements in grid patterns and the craft of weaving, an activity traditionally dominated by women."

This is a structural historical argument: the logic of the grid is the logic of weaving; weaving was for most of its history women's work; therefore the female dominance in systematic/generative art is not coincidental but continuous with a longer history. Algorithmic archaeology here is also the archaeology of an unacknowledged lineage. "Angles Morts" — blind spots — names exactly what this lineage has been in art history.

Albers' invisible algorithm — archaeology as forensics

The Angles Morts catalogue provides the most concrete account of how algorithmic archaeology works in practice:

"While it would have been technically easy to programme an algorithm that replicated her triangle patterns, it turned out that randomly placing the triangles did not achieve the desired aesthetic effect. Van den Dorpel realised that Albers must have formulated clear rules in her head, which she applied algorithmically to each cell of the grid. This realisation led to a deep respect for the invisible structures and thought processes hidden in the works of Albers and other female generative artists."

The procedure: (1) attempt computational replication; (2) discover the random version fails aesthetically; (3) conclude that non-random rules must be implicit in the original. The algorithm is deduced from the failure to replicate without it. This is forensic archaeology: the algorithm is not written down anywhere — it is reconstructed from the evidence of its effects.

The result is not a copy of Albers but an understanding of her practice as already algorithmic. The respect comes from the discovery that what appeared to be aesthetic intuition was in fact systematic rule-application — invisible not because it was concealed but because it was never written as code. The code existed as embodied procedural knowledge, applied by hand to each cell. Making this visible required the failure of random replication as a diagnostic tool.

This forensic understanding of Albers' grid-based rules forms the root of a formal lineage: Albers' embodied algorithm → the Anni works (rules made explicit in software, scan direction referencing the weaving process) → the Stained Unravel series (cellular automaton rules operating autonomously in time, vastly greater emergence). All three are systems of modular geometric elements placed in a grid, operating under different rules, producing vastly varying complexity. See Senescenence, "The grid family: Albers, Anni, Stained Unravel."